Autonomous, closed-loop and adaptive simulated annealing based machine learning approach for intelligent analytics-assisted self-organizing-networks (SONs)

ABSTRACT

Convergence times associated with simulated annealing based (SA-based) optimization in wireless networks can be reduced by introducing an additional local or cell-level evaluation step into the evaluation of global solutions. In particular, new local solutions may be evaluated based on local performance criteria when the new solutions are in a global solution deemed to have satisfied a global performance criteria. New local solutions that satisfy their corresponding local performance criteria remain in the new global solution. New local solutions that do not satisfy their corresponding local performance criteria are replaced with a corresponding current local solution from a current global solution, thereby modifying the new global solution. The resulting modified global solution includes both new local solutions and current local solutions prior to being accepted as the current global solution for the next iteration.

The present application is a Continuation-In-Part of and claims priorityto U.S. Non-Provisional patent application Ser. No. 14/757,764 filed onDec. 23, 2015, which is a Continuation-In-Part of U.S. Non-Provisionalpatent application Ser. No. 14/971,870 filed on Dec. 16, 2015, which isa continuation-in-part of and claims priority to U.S. non-provisionalpatent application Ser. No. 14/963,062 filed on Dec. 8, 2015, whichclaims priority to the following U.S. provisional applications:

U.S. Provisional Application No. 62/089,654 filed Dec. 9, 2014;

U.S. Provisional Application No. 62/096,439 filed Dec. 23, 2014;

U.S. Provisional Application No. 62/093,283 filed Dec. 17, 2014;

U.S. Provisional Application No. 62/099,854 filed Jan. 5, 2015; and

U.S. Provisional Application No. 62/100,003 filed Jan. 5, 2015.

All of these are hereby incorporated herein by reference

TECHNICAL FIELD

The present invention relates to telecommunications, and, in particularembodiments, to techniques for autonomous, closed-loop and adaptivesimulated annealing based machine learning approach for intelligentanalytics-assisted Self-Organizing-Networks (SONs).

BACKGROUND

Modern mobile telecommunication networks are becoming larger and morecomplex, as the industry migrates towards densely-deployed networksincluding large numbers of highly concentrated cells capable ofproviding near-ubiquitous coverage, as well as heterogeneous networks(Het-Nets) capable of supporting different air-interface technologies.As mobile networks grow larger and more complex, they becomeincreasingly difficult to manage and operate, as control decisions areoftentimes made based on incomplete, stale, and, in some cases,inaccurate information. Due to their increased scale and complexity, itmay also be more challenging to identify, diagnose, and troubleshootquality and performance related issues, such as those related tocoverage and capacity, interference, and mobility. To make thesechallenges more manageable, Self-Organizing-Network (SON) automationtechnology is being developed.

SUMMARY OF THE INVENTION

Technical advantages are generally achieved, by embodiments of thisdisclosure which describe techniques for autonomous, closed-loop andadaptive simulated annealing based machine learning approach forintelligent analytics-assisted Self-Organizing-Networks (SONs).

In accordance with an embodiment, a method for adjusting communicationparameters in a multi-cell wireless network is provided. In thisexample, the method iteratively generates and evaluates global solutionsfor a wireless network over a sequence of iterations. Each of the globalsolutions includes multiple local solutions that specify wirelessconfiguration parameters for local coverage areas in the wirelessnetwork. Each of the global solutions is evaluated using a simulatedannealing based (SA-based) optimization algorithm. At least some of thelocal solutions are evaluated separately from their corresponding globalsolution. The method further includes selecting one of the globalsolutions when an output condition is reached, and sending localsolutions in the selected global solution to access points (APs) in thewireless network.

In one example, iteratively generating and evaluating the globalsolutions comprises evaluating each of the global solutions during acorresponding iteration using the SA-based optimization algorithm, andevaluating local solutions in corresponding global solutions thatsatisfy a global performance criteria. In such an example, evaluation ofthe local solutions may include determining whether the local solutionssatisfy a local performance criteria, and replacing, in thecorresponding global solutions, local solutions that fail to satisfy thelocal performance criteria with previous local solutions. The localsolutions may satisfy a local performance criteria when a cost of thelocal solution is less than a threshold or when a function of a cost ofthe local solution and a local temperature parameter of the SA-basedoptimization algorithm is less than a threshold.

In an example, iteratively generating and evaluating the globalsolutions further comprises updating a global temperature parameter ofthe SA-based optimization algorithm based on an evaluation of a globalsolution during an instant iteration, and updating one or more localtemperature parameters of the SA-based optimization algorithm based onevaluations of one or more local solutions during the instant iteration.

In another example, iteratively generating and evaluating the globalsolutions over the sequence of iterations further comprises generating afirst new global solution during a first iteration by finding neighborsof current local solutions in a current global solution, identifying oneor more new local solutions in the first new global solution that failto satisfy local performance criteria, and replacing, in the first newglobal solution, the one or more new local solutions with correspondingones of the current local solutions from the current global solution. Insuch an example, iteratively generating and evaluating the globalsolutions over the sequence of iterations further comprises generating asecond new global solution during a second iteration by findingneighbors of local solutions in the modified global solution. Anapparatus for performing these methods is also provided.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention, and theadvantages thereof, reference is now made to the following descriptionstaken in conjunction with the accompanying drawing, in which:

FIG. 1 illustrates a diagram of an embodiment wireless communicationsnetwork;

FIG. 2 illustrates a diagram of another embodiment wirelesscommunications network comprising a cluster of cells;

FIG. 3 illustrates a flowchart of an embodiment method for adjustingwireless configuration parameters in a wireless network using anSA-based optimization algorithm; and

FIG. 4 illustrates a flowchart of another embodiment method foradjusting wireless configuration parameters in a wireless network usingan SA-based optimization algorithm;

FIG. 5 illustrates a flowchart of an embodiment method for adjustingcommunication parameters for a cluster of cells using an autonomousadaptive simulated annealing algorithm;

FIG. 6 illustrates an example process for optimizing cell specificantenna configuration parameters;

FIG. 7 illustrates a graph depicting network performance as a functionof power and downtilt parameters;

FIG. 8 illustrates an example process for determining cell states toadjust antenna configuration parameters;

FIG. 9 illustrates the coverage states that can be assigned to a cell;

FIG. 10 illustrates additional coverage states of a weak edge state anda weak interior/insufficient state that can be assigned to a cell;

FIGS. 11A-11B illustrate a process for determining the coverage statefor a cell;

FIG. 12 illustrates the overshooting states that can be assigned to acell;

FIG. 13 illustrates an example of a cell in an overshooter state;

FIG. 14 illustrates a graph depicting a relationship between overlappedUE devices and overshooting identification;

FIG. 15 shows a process for determining an overshooter state of a cell;

FIG. 16 shows the interference states that can be assigned to a cell;

FIG. 17 shows a process for determining an interferer state of a cell;

FIG. 18 shows the quality states that can be assigned to a cell;

FIG. 19 shows a process for determining a quality state of a cell;

FIG. 20 illustrates a flowchart of an embodiment method for adjustingcommunication parameters for a subset of cells;

FIG. 21 illustrates a graph depicting simulation results obtained byperforming the method described in FIG. 20;

FIG. 22 illustrates another graph depicting additional simulationresults obtained by performing the method described in FIG. 20;

FIG. 23 illustrates a flowchart of an embodiment method for adjustingcell configurations in a wireless network;

FIGS. 24A-24C illustrates an embodiment table for mapping status labelsto actions;

FIGS. 25A-25C illustrates another embodiment table for mapping statuslabels to actions;

FIG. 26 illustrates a diagram of another embodiment wireless network;

FIG. 27 illustrates a flowchart of an embodiment method for adjustingcell configurations in a wireless network;

FIG. 28 illustrates a flowchart of another embodiment method foradjusting cell configurations in a wireless network;

FIG. 29 illustrates a graph of an embodiment up-blame and down-blamespace;

FIG. 30 illustrates a flowchart of another embodiment method foradjusting cell configurations in a wireless network;

FIG. 31 illustrates a diagram of an embodiment antenna radiationcoverage zone;

FIG. 32 illustrates a table of an embodiment blame counter matrix;

FIG. 33 illustrates a diagram of a graph problem & solutionvisualization;

FIG. 34 illustrates a flowchart of an embodiment method for operating aCCO interface;

FIG. 35 illustrates a diagram of an embodiment controller adapted toadjust wireless configuration parameters in a wireless network;

FIG. 36 illustrates a diagram of an embodiment processing system; and

FIG. 37 illustrates a diagram of an embodiment transceiver.

Corresponding numerals and symbols in the different figures generallyrefer to corresponding parts unless otherwise indicated. The figures aredrawn to clearly illustrate the relevant aspects of the embodiments andare not necessarily drawn to scale.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The making and using of embodiments of this disclosure are discussed indetail below. It should be appreciated, however, that the presentdisclosure provides many applicable inventive concepts that can beembodied in a wide variety of specific contexts. The specificembodiments discussed are merely illustrative of specific ways to makeand use the various embodiments disclosed herein, and do not limit thescope of the disclosure.

Simulated annealing based (SA-based) optimization algorithms may be usedto evaluate global solutions for multi-cell wireless networks. As usedherein, the term “global solution” refers to a set of local solutionsfor two or more wireless network coverage areas in a wireless network.Each “local solution” specifies one or more wireless configurationparameters for a particular wireless network coverage area. For example,in the context of coverage and capacity optimization (CCO), a localsolution may specify an antenna tilt of an access point in a wirelessnetwork coverage area and/or a transmit power level (e.g., uplink,downlink, or otherwise) for the wireless network coverage area.

When conventional SA-based optimization is used in a wireless network,global solutions are generated and evaluated over a sequence ofiterations. In particular, a new global solution is generated duringeach iteration by finding neighbors of a current global solution usingthe SA-based algorithm, and then evaluated to determine whether the newglobal solution satisfies the global performance criteria. If so, thenew global solution is accepted, thereby replacing the current globalsolution for the next iteration. If not, the new global solution isdiscarded, and the current global solution stays the same for the nextiteration.

Evaluation of the new global solution generally entails determiningwhether a function of a cost of the new global solution and anacceptance probability of the SA-based algorithm is below a threshold.The function of the cost and the acceptance probability may generally bereferred to as the acceptance probability functions, and the thresholdfor evaluating the new global solution is typically set based on a costof the current global solution. The acceptance probability allows a newglobal solution to be accepted even if the cost of the new globalsolution exceeds the cost of the current global solution. This allowsthe SA-based optimization to avoid being trapped in local maximums.Conventional SA-based optimization schemes have been found to have longconvergence times when used in multi-cell wireless networks.Additionally, conventional SA-based optimization schemes often introducesignificant performance/quality degradation (e.g., KPI/KQI reduction)when used online during runtime operation of the wireless network.

Aspects of this disclosure provide several techniques for reducingconvergence times of SA-based optimization. One such techniqueintroduces an additional local or cell-level evaluation step into theevaluation of the global solutions. More specifically, embodiments ofthis disclosure evaluate new local solutions in a new global solutionwhen the new global solution is deemed to have satisfied the globalperformance criteria. Throughout this disclosure, local solutions in anew global solution generated during an instant iteration are referredto as “new local solutions,” while local solutions from a current globalsolution brought into the instant iteration are referred to as “currentlocal solutions.” The new local solutions are evaluated based on localperformance criteria. New local solutions that satisfy theircorresponding local performance criteria remain in the new globalsolution. New local solutions that do not satisfy their correspondinglocal performance criteria are replaced with a corresponding currentlocal solution from the current global solution, thereby modifying thenew global solution. The resulting global solution, referred to hereinas a “modified global solution,” therefore includes both new localsolutions that satisfy their corresponding local performance criteriaand current local solutions that replaced new local solutions thatfailed to satisfy their local performance criteria. In this way, themodified global solution includes both new local solutions generatedduring the instant iteration and current local solutions from thecurrent global solution brought into the instant iteration. The modifiedglobal solution is then accepted as the current solution for the nextiteration. Once a new or modified global solution is accepted, itreplaces the current global solution, and all local solutions thereintransition into “current local solutions” for the next iteration.

Various techniques can be used to evaluate new local solutions inqualifying global solutions. In one example, a cost for a new localsolution is calculated using an objective function, and then comparedagainst a local cost threshold. New local solutions whose cost is lessthan the cost threshold remain in the new global solution, while newlocal solutions whose cost exceeds the local cost threshold are replacedby corresponding current local solutions from the current globalsolution. In another example, new local solutions are evaluated based onan acceptance probability function. In such an example, each new localsolution in a qualifying global solution is evaluated by determiningwhether a function of a cost of the new local solution and a localacceptance probability exceeds a threshold. In both examples, thethreshold for evaluating the new local solutions may be based on a costof a corresponding current local solution from the current globalsolution or an absolute/predefined cost (e.g., a cost associated with aminimum level of acceptable performance). In yet another example, kPIsand/or KQIs (e.g., call drop rates, radio access bearer (RAB) successrates, handover success rates, interference indicators) relevant to anew local solution and/or other new neighboring local solution (e.g.,local solutions for neighboring cells) may be used to evaluate the newlocal solution.

Another technique for reducing convergence times during SA-basedoptimization generates and evaluates local solutions for fewer than allcoverage areas in a wireless network during at least some iterations.This may target the optimization to a smaller group of local coverageareas exhibiting a common characteristic (e.g., cost, etc.). In oneexample, local coverage areas exhibiting a cost that exceeds a thresholdduring a previous period are selected for inclusion in a subset of localcoverage areas for which local solutions are generated, evaluated, andoptimized during one or more iterations. This may focus the SA-basedoptimization on improving the performance of the worst-performingcoverage areas.

Yet another technique for reducing convergence times during SA-basedoptimization utilizes an iterative learning approach to exploitknowledge and experience obtained from evaluating global and localsolutions during previous iterations. This may be achieved by using theevaluation results to update parameters (e.g., global/local accessprobability, global/local temperature parameters, etc.) of an SA-basedoptimization algorithm, which is then used to generate and/or evaluateglobal and/or local solutions in subsequent iterations. In one example,a local step size is decreased when the cost of an evaluated localsolution is below a certain threshold. In another example, a local stepsize is decreased when interference indicators (e.g., blame,interference factors calculated form MRs) for neighboring localsolutions increases above a certain threshold. In another example, alocal step size is increased when neighboring local solutions exhibitsimilar costs, or when the cost increase/decrease after a certain numberof iterations is below a threshold. This may increase the rate in whichthe SA-based algorithm climbs out of a local maximum. In anotherexample, an access probability is modified when neighboring localsolutions exhibit similar costs, or when the cost increase/decreaseafter a certain number of iterations is below a threshold. This mayallow higher cost local solutions to be accepted as a normal solution,which in turn may increase the rate in which the SA-based algorithmclimbs out of a local maximum. The step size and/or access probabilitiesmay be modified indirectly by modifying a local temperature parameter.Some of the aforementioned techniques may also reduce the likelihood ofintroducing significant performance degradation during online iterativeoptimization of the wireless network. These and other aspects areexplained in greater detail below.

FIG. 1 is a diagram of a wireless network 100 for communicating data.The wireless network 100 includes a base station 110 having a wirelesscoverage area 101, a plurality of mobile devices 120, a backhaul network130, and a controller 190. As shown, the base station 110 establishesuplink (dashed line) and/or downlink (dotted line) connections with themobile devices 120, which serve to carry data from the mobile devices120 to the base station 110 and vice-versa. Data carried over theuplink/downlink connections may include data communicated between themobile devices 120, as well as data communicated to/from a remote-end(not shown) by way of the backhaul network 130. As used herein, the term“base station” refers to any component (or collection of components)configured to provide wireless access to a network, such as an evolvedNodeB (eNB), a macro-cell, a femtocell, a Wi-Fi access point (AP), orother wirelessly enabled devices. Base stations may provide wirelessaccess in accordance with one or more wireless communication protocols,e.g., long term evolution (LTE), LTE advanced (LTE-A), High Speed PacketAccess (HSPA), Wi-Fi 802.11a/b/g/n/ac. As used herein, the term “mobiledevice” refers to any component (or collection of components) capable ofestablishing a wireless connection with a base station, such as a userequipment (UE), a mobile station (STA), relay, device engaging inmachine type communications, or other wirelessly enabled devices. Thecontroller 190 may be any component, or collection of components,adapted to perform network optimization for the wireless coverage area101. The controller 190 may be co-located with the base station 110.Alternatively, the controller 190 may be separate and distinct from thebase station 110, in which case the controller 190 may communicate withthe base station over the backhaul network 130. In some embodiments, thenetwork 100 may comprise various other wireless devices, such as lowpower nodes, etc.

FIG. 2 illustrates a wireless network 200 comprising local coverageareas 201, 202, 203, 204, 205 within which wireless access is providedto mobile devices by base stations 210, 220, 230, 240, 250(respectively). It should be appreciated that the wireless network 200is shown as including five local coverage areas 201, 202, 203, 204, 205for purposes of brevity and clarity, and that inventive aspects providedcan be used in wireless network having any number of local coverageareas. It should also be appreciated that, in some implementations, thewireless network 200 may be a heterogeneous network (Het-Net) in whichat least some of the base stations 210, 220, 230, 240, 250 communicateusing different wireless access technologies.

Modifying wireless configuration parameters in one of the local coverageareas 201, 202, 203, 204, 205 may affect another performance in thatlocal coverage area as well as the other local coverage areas. Forexample, increasing a transmit power level in the local coverage area205 may improve coverage and capacity in the local coverage area 205,while also increasing inter-cell-interference in the local coverageareas 201, 202, 203, 204. Wireless configuration parameters in the localcoverage areas 201, 202, 203, 204, 205 may also complement one anotherin a manner that affects the overall performance of the wirelessnetwork. By way of example, the hysteresis margins of neighboring localcoverage areas 201, 202, 203 204, 205 may affect mobility load balancing(MLB) and mobility robustness optimization (MRO) performance over theentire wireless network 200.

The controller 290 may generate global solutions for the wirelessnetwork 200 using an SA-based optimization algorithm. In someembodiments, the controller 290 performs on-line SA-based optimizationsuch that the new global solutions are implemented in the network 200during test periods, and then the global solutions and/or localsolutions are evaluated based on information (e.g., measurement report(MR) information, etc.) associated with wireless transmission in thewireless network 200 during the test period.

Aspects of this disclosure provide embodiment SA-based optimizationtechniques that evaluate global solutions at both the global level andlocal level. FIG. 3 illustrates an embodiment method 300 for adjustingcommunication parameters in a multi-cell wireless network using anSA-based optimization algorithm, as may be performed by a controller. Atstep 310, the controller generates or identifies an initial globalsolution, and accepts it as the current global solution. In someembodiments, the initial global solution is the global solution that wasused to operate the wireless network prior to the initial iteration ofthe SA-based optimization. In other embodiments, the initial globalsolution is a predefined solution. In yet other embodiments, the initialglobal solution is generated based on initial parameters (e.g., initialglobal temperature, etc.) of the SA-based optimization algorithm. Othertechniques may also be used to generate the initial global solution,e.g., random search, heuristic, etc.

At step 320, the controller generates a new global solution based on theSA-based optimization algorithm. The new global solution may begenerated in various ways. In one embodiment, new global solution isgenerated by finding neighbors of current local solutions in the currentglobal solution according to parameters (e.g., temperature, etc.) of theSA-based optimization algorithm. For example, a neighbor of a currentlocal solution may be found by modifying one or more wireless parametersspecified by the local solution according to a step size and directiondefined by a local temperature parameter of the SA-based optimizationalgorithm. In other embodiments, the direction and step size in a localsolution may be generated based on domain or system knowledge (e.g.,based on the labels or problematic types of the cell). In yet anotherembodiments, the direction and step size in a local solution may begenerated based on a guided random search (e.g., multivariate Gaussianor Cauchy distribution) by iteratively adapting the relevant keyparameters (e.g., covariances) used in guided random search to the costof individual local solution based on the feedback from the realnetwork. In yet another embodiment, offline simulation may be updatedand used to select the step size and direction for a local solutionbased on the feedback from the real network per iteration. In yetanother embodiment, reinforcement learning techniques may be used togenerate the step size and direction for local solution based on thefeedback from the network.

At step 330, the controller determines whether the new global solutionsatisfies a global performance criteria. In some embodiments, this isachieved by evaluating an online performance of the new global solutionin the wireless network during a test period. For example, new localsolutions in the new global solution may be communicated to APs in thewireless network, and used to transmit or receive wireless transmissionsduring the test period. Information (e.g., measurement reports (MRs),etc.) associated with the wireless transmissions may then becommunicated to the controller, and used to calculate a global cost forthe global solution based on an objective function. Details regardingobjective functions are provided below. The controller then maydetermine whether the new global solution satisfies the globalperformance threshold based upon whether an acceptance probabilityfunction of the cost of the new global solution is below a globalthreshold. The global threshold may be based on a cost of the currentglobal solution. If the new global solution satisfies the globalperformance criteria, then the method 300 proceeds to step 340.Otherwise, if the new global solution does not satisfy the globalperformance criteria, then the new global solution is discarded, and themethod 300 proceeds to step 380.

At step 340, the controller determines whether one of the new localsolutions in the new global solution satisfies a local performancecriteria. Various evaluation techniques may be used to determine whetherthe local solutions satisfy the local performance criteria, anddifferent local performance criteria may be used to evaluate new localsolutions for different local coverage areas in a wireless network. Inan embodiment, new local solutions are evaluated on a cost-basis. Insuch an embodiment, a new local solution is deemed to satisfy the localperformance criteria if a cost of the local solution is below a costthreshold. In another embodiment, new local solutions are evaluatedusing an SA-based criteria. In such an embodiment, a new local solutionis deemed to satisfy the local performance criteria if an accessprobability function of the cost of the new local solution is below acost threshold. In both embodiments, the cost of new local solutions maybe computed using an objective function. The cost thresholds may bebased on an absolute threshold (e.g., a minimum performance level foreach cell). Alternatively, the cost thresholds may be based on arelative threshold. For example, a cost threshold may be based on a cost(or adjusted cost) of a corresponding current or previous localsolution. As another example, the cost threshold may be based on costsassociated with a certain percentage of current or previous localsolutions (e.g., cost of bottom ten percent of local solutions, etc.).

If the new local solution is deemed to satisfy the local performancecriteria at step 340, then the method 300 proceeds to step 360.Otherwise, if the new local solution does not satisfy the localperformance criteria, then the method proceeds to step 350, where thecontroller replaces the new local solution with a corresponding currentlocal solution. At step 360, the controller determines whether there aremore new local solutions to evaluate. If so, the method 300 reverts backto step 340. Once all new local solutions in a new global solution havebeen evaluated, the method proceeds to step 370, where the controlleraccepts the new or modified global solution as the current solution.

At step 380, the controller determines whether an output condition hasbeen reached. Various criteria could be used to determine whether anoutput condition has been reached. In one embodiment, the outputcondition is reached after a threshold number of iterations have beenperformed. In other embodiments, the output condition is reached when anoptimization criteria is satisfied. For example, the output conditionmay be reached when an optimization parameter (e.g., stop temperature,stop probability) reaches or crosses a threshold. As another example,the output condition may be reached when a maximum or minimum costcrosses a threshold. As yet another example, the output condition may bereached when the amount of improvement (e.g., cost reduction) over athreshold number of iterations does not exceed a threshold (e.g., lessthan two percent cost reduction in best solution over twentyiterations). As yet another example, the output condition may be reachedwhen no improvement occurs over a threshold number of iterations (e.g.,best global solution remains the same for ten iterations). If the outputcondition has not been reached, the instant iteration is incremented tothe next iteration, and the method 300 reverts back to step 320. If theoutput condition has been reached, then the controller selects the bestglobal solution, and sends local solutions in the best global solutionsto APs in the wireless network at step 390.

In some embodiments, parameters of the SA-based optimization algorithmare updated after incrementing an iteration based on the evaluation ofglobal and local solutions during one or more previous iterations. Inone example, a local step size is decreased when the cost of localsolution exhibit relative low cost or when the cost is below a certainthreshold. In another example, a local step size is decreased wheninterference indicators (e.g., blame, interference factors calculatedform MRs) for neighboring local solutions increases above a certainthreshold. In another example, a local step size is increased whenneighboring local solutions exhibit similar costs, or when the costincrease/decrease after a certain number of iterations is below athreshold. This may increase the rate in which the SA-based algorithmclimbs out of a local maximum. In another example, an access probabilityis modified when neighboring local solutions exhibit similar costs, orwhen the cost increase/decrease after a certain number of iterations isbelow a threshold. This may allow higher cost local solutions to beaccepted as a normal solution, which in turn may increase the rate inwhich the SA-based algorithm climbs out of a local maximum. The stepsize and/or access probabilities may be modified indirectly by modifyinga local temperature parameter.

Aspects of this disclosure provide embodiment SA-based optimizationtechniques that generate and evaluate local solutions for fewer than allsubsets of local coverage areas in a wireless network during at leastsome iterations. This allows the optimization to focus on fewer numbersof local coverage areas during a given iteration, thereby increasing therate of improvement in those local coverage areas.

FIG. 4 illustrates an embodiment method 400 for adjusting communicationparameters in a multi-cell wireless network using an SA-basedoptimization algorithm, as may be performed by a controller. At step410, the controller selects a subset of local coverage areas in awireless network for which to generate local solutions during one ormore iterations. The subset of local coverage areas may be selectedbased on different criteria. In one embodiment, local coverage areas areselected for inclusion in the subset of wireless coverages based on acost associated with wireless transmissions in the local coverage areas.For example, the controller may rank the local coverage areas based oncost, and then select the top K coverage areas for inclusion in thesubset (where K is an integer greater than one). In such an example, thesame number of local solutions would be generated and evaluated duringeach iteration. As another example, the controller may select any localcoverage area having a cost that is above a threshold for inclusion inthe subset of local coverage areas.

At step 420, the controller generates and evaluates local solutions forthe subset of local coverage areas using an SA-based optimizationalgorithm during an instant iteration. During step 420, the controllermay generate a subset of local solutions for the subset of coverageareas in a manner similar to that used to generate the global solutionin step 320, and then evaluate them on the group level, the local level,or both.

In some embodiments, the controller evaluates the subset of localsolutions at the group level without evaluating them at the local level.For example, the controller may evaluate the subset of local solutionsto determine whether they collectively satisfy a group or globalperformance criteria in a manner similar to that used to evaluate thenew global solution in step 330. In such embodiments, the subset oflocal solutions stand or fall together.

In other embodiments, the controller evaluates local solutions in thesubset of local solutions at the local level without evaluating thesubset of local solutions at the group level. For example, thecontroller may evaluate local solutions in the subset of local solutionsindividually based on local performance criteria in a manner similar tothat used to evaluate the new local solutions in step 360. In suchembodiments, local solutions in the subset of local solutions may standor fall by themselves.

In yet other embodiments, the controller evaluates the subset of localsolutions at both the group level and the local level. For example, thecontroller may evaluate the subset of local solutions in a mannersimilar to that described in steps 330-370.

At step 430, the controller determines whether a subset terminationcondition has been reached. The criteria used to determine whether thesubset termination condition may be similar to those used to determinewhether the output condition was reached in step 380. For example, thesubset termination condition may be reached after local solutions forthe subset of local coverage areas have been generated and evaluated fora threshold number of iterations. As another example, the subsettermination condition may be reached when an optimization parameter(e.g., stop temperature, stop probability, etc.) associated with thesubset of coverage areas reaches or crosses a threshold or when anoutput condition (e.g., cost reduction) associated with the subset ofcoverage areas experiences marginal or no improvement over a certainnumber of iterations, e.g., less than two percent cost reduction in bestsubset of local solutions over twenty iterations, best subset of localsolutions remains the same for ten iterations, etc.

If the subset termination condition has not been reached, then theiteration is incremented, and the method 400 reverts back to step 420.Once the subset termination condition has been reached, the methodproceeds to step 480, where the controller determines whether an outputcondition has been reached. The criteria used to determine whether theoutput condition has been reached in step 480 may be similar to thoseused to determine whether the output condition was reached in step 380.If the output condition has not been reached, then the iteration isincremented, and the method 400 reverts back to step 410, where thecontroller selects a new subset of local coverage areas for optimizationduring the next subset of iterations. Once the output condition has beenreached, the controller selects the best local solutions, and sends themto APs in the wireless network at step 490.

Embodiments of this disclosure discuss computing costs for global andlocal solutions based on objective functions. Throughout thisdisclosure, the term “objective function” is primarily discussed in thecontext of a loss function, in which lower resulting values (e.g., lowercosts) are indicative of better solutions than higher resulting values.It should be appreciated that inventive aspects may also use utilityfunctions, in which higher resulting values (e.g., higher utilities) areindicative of better solutions than lower resulting values.

Objective functions may be any function that computes a cost or utilityassociated with a local coverage area, a subset of local coverage areas,or a wireless network. An example of a generic objective function forcomputing a local, group, or global cost is as follows: Cost=Σ_(i=1)^(n) w_(i)*C_(i), where C_(i) is an i^(th) component and w_(i) is aweighting factor for the i^(th) component. Objective function componentscan be a function of a key performance indicator (KPI) or a key qualityindicator (KQI). Examples of possible KPIs and KQIs for variousoptimization problems are provided in Table 1.

Objective functions for calculating global or group costs may includestatistical components. For example, a generic global objective functionis as follows: Cost=w_(i)*cells(result_x_(i)), where w_(i) is aweighting factor, cells(result_x_(i)) is a number, percentage, or ratioof cells experiencing a given result (result_x_(i)). In such an example,the given result(s) could include the number or percentage of cellsexperiencing a relative result (e.g., cost reduction exceedingthreshold, cost increase exceeds threshold, etc.) or an absolute result(e.g., overall cost below a threshold, overall cost below a threshold,overall cost within or outside a range, etc.).

TABLE 1 Optimization Problem KPIs/KQIs Coverage Capacity Referencesignal received power (RSRP), Optimization (CCO) RSRQ, RSSINR, RRCSuccess Rate, RAB Success Rate, Handover Success Rate, Call Drop RateInter-cell-interference RSRP, RSRQ, RSSINR, , RRC Success Rate,Coordination (ICIC) RAB Success Rate, Handover Success Rate, Call DropRate Mobility Load RSRP, RSRQ, RSSINR, RB Utilization, RRC Balancing(MLB) Success Rate, RAB Success Rate, Handover Success Rate, Call DropRate Mobility Robustness RSRP, RSRQ, RSSINR, RRC Success Rate,Optimization (MRO) RAB Success Rate, Handover Success Rate, Call DropRate Cell Outage RSRP, RSRQ, RSSINR, , RRC Success Rate, Compensation(COD) RAB Success Rate, Handover Success Rate, Call Drop Rate

The following provide some illustrative examples of objective functionsfor various optimization problems. These examples represent only some ofthe possible examples, and should not be interpreted as limiting.

One example of an objective function for calculating a local, group, orglobal cost during coverage capacity optimization or cell outagecompensation is as follows:Cost=w₁*Num_MRs(RSRP≤Thr_rsrp)+w₂*Num_MRs(INT≥thr_int), where w₁ and w₂are weighting factors, Num_MRs (RSRP≤Thr_rsrp) is the number ofmeasurement reports (MR) indicating RSRP level below an RSRP thresholdduring a fixed period, and Num_MR(INT≥thr_int) is the number ofmeasurement reports (MR) indicating an interference level below aninterference threshold during the fixed period. In such an example, theinterference levels may correspond to signal to interference plus noise(SINR) levels obtained by measuring reference signals.

Another example of an objective function for mobility load balancingproblem may be a certain weighted function of the following measuredvalues: RSRP and interference (SINR or RSRQ) for each MR, and RButilization and/or throughput for each cell, with some constraints(e.g., threshold for RSRP or RSRQ, etc).

Another example of the objective function for inter-cell interferencereduction may be a certain weighted function of the receivedinterference at the subcarrier of the user that is caused by interferingcell.

Different wireless configuration parameters may be adjusted fordifferent SA-based optimization algorithms. Examples of wirelessconfiguration parameters for different optimization problems areprovided in Table 2.

TABLE 2 Optimization Problem Wireless configuration parameters CoverageCapacity RFs (e.g., antenna tilt, azimuth), transmit Optimization (CCO)power Inter-cell-interference sub-band power factor edge-to-centerCoordination (ICIC) boundary, power, RF (e.g., tilt) Mobility Load pilotpower, RFs (e.g., tilt), Handover Balancing (MLB) parameters (e.g.,hysteresis, cell individual offset) Mobility Robustness pilot power, RFs(e.g., tilt), Handover Optimization (MRO) parameters (e.g., hysteresis,cell individual offset) Cell Outage RFs (e.g., antenna tilt, azimuth),transmit Compensation (COD) power

Aspects of this disclosure provide autonomous adaptive simulatedannealing algorithms. An embodiment algorithm is described by thefollowing ten steps. The first step comprises obtaining an initialsolution (S) and an initial temperature (T₀). In one embodiment, thestarting temperature (T₀) is selected based on an objective or costfunction according to the online feedback from the network or from anoffline simulation. In another embodiment, the starting temperature (T₀)is selected by increasing the starting temperature (T₀) until anacceptance ratio exceeds a threshold, e.g., ninety percent, etc.

The second step comprises evaluating the cost of the initial solutionusing constraints (e.g., thresholds and weights for parameters (e.g.,RSRP, SINR) used in objective function). This may include anormalization process that considers the cost per cell, the ratio oftotal cost to the total number of MRs, and the ratio of cost to numberof MRs per cell. The second step may also consider the cost per cell orper area (e.g., all cells or partial group of cells such as neighbors),cost percentage (e.g., ratio of cost per cell to MR number per cell),and distribution (e.g., weighted by cell).

The third step comprises generating a new solution (S_(new)). The newsolution may be generated using various adaptive (e.g., on-line)algorithm algorithms, including a uniform algorithm, a guided randomsearch (e.g., Gaussian, Cauchy). The new solution may also be generatevia an offline simulation combined with reinforcement learning.Generating the new solution may include selecting which cell(s) are tobe adjusted. The cells may be chosen randomly, using a heuristicapproach, e.g., sorted by cost to MR no per cell, first m, exponentialprobability), or a using a hybrid approach (e.g., part random and partheuristic). The number of cells that are optimized may fixed (e.g., Xnumber of cells), or adaptive (e.g., based on the priority or severityof problematic cells). One or more parameters may be adjusted periteration. Various change/action/perturbation mechanisms may be used toapplied to adjust the parameters to be adjusted. For example, parametersmay be adjusted in the positive or negative direction. The adjustmentscan use different step size adjustment parameters, e.g., small step,large step, absolute step size, relative step size, fixedstep-size/range, adaptive step-size/range depending on the temperatureat system/cell level or offline simulation, etc.

The fourth step includes evaluating the cost of the new solution. Thefifth step includes determining whether to select the new solution asthe current solution. This decision may consider various criteria, andmay be probability-based and/or threshold based. For example, thedecision may consider criteria related to the cost of the new solution,e.g., difference between the cost of new solution and optimal cost, costper MR or per cell, etc.

The sixth step determines whether an equilibrium condition (# ofiterations carried out before update T) has not been reached. If not,then the technique reverts back to step three. The seventh stepcomprises learning from experience gained during the first six steps,e.g., feedback from the system, mistake, reward, etc. This step mayupdate models and/or parameters, such as control parameters (e.g.,system/cell level temperate Tn), propagation models used by simulators,engineering parameters, parameters/models for identifying problematiccells, generating new solution and accepting new solution, etc.

The eight step determines whether a backward/safeguard condition hasbeen met. If so, the technique back-steps to a previous solutionaccording to some criteria. This step may be helpful in avoiding locallyoptimal solutions. The ninth step determines whether a terminationcriterion has been reached according to some criteria. If not, then thetechnique reverts back to step three. The tenth step returns allsolutions and relevant parameters, e.g., best solution and cost so far,current solution and cost, etc.

FIG. 5 illustrates an embodiment flowchart for adjusting communicationparameters for a cluster of cells using an autonomous adaptive simulatedannealing algorithm. As shown, the method begins by identifying allproblematic cells. Next, the method generates subgroups of cells to beoptimized. Thereafter, the method selects subgroups of cells to beoptimized in parallel and/or subgroups of cells to be optimizedsequentially. Subsequently, the method selects cells to be optimized ineach subgroup. Next, the method generates a new solution. Thereafter,the method determines whether or not to select the new solution at thesystem level.

If the new system is rejected at the system level according to somecriteria (e.g., metropolis criterion, threshold, difference betweenobjective function for new solution and that for best/current solution),then the method reverts back to another solution, e.g., most recent“best” solution, current solution, etc. If the new solution is acceptedat the system level, then the method determines whether or not to acceptthe new solution at the cell level for each cell according to somecriteria (e.g., metropolis criterion, threshold, difference betweenobjective function for new solution and that for best/current solutionat cell level). If the new solution is accepted for all the cells at thecell level, then the method proceeds to learn from its experience. Ifthe new solution is rejected for some cells at cell level, then themethod reverts back to some other solution (e.g., latest best RFparameter, current RF), for those rejected cells, prior to learning fromthe experience. When learning from the experience, the method may recordthe solution, and update the models/parameters/statistics accordingly.After learning from the experience, the method determines whether toterminate the optimization of the subgroup. If the optimization for thesubgroup is not terminated, then the method re-selects cells to beoptimized in this subgroup. If the optimization for this subgroup isterminated according to some criteria (e.g., threshold, KPIs), then themethod outputs the best solution, and then determines whether toterminate the SON session.

Aspects of this disclosure provide techniques for dynamically adjustingcell-specific radio frequency (RF) configuration parameters (e.g.,electrical antenna tilt, reference symbol (RS) pilot power, etc.) tooptimize an objective function. In one embodiment, RF parameters of asingle cell are adjusted to maximize a per-cell performance metric. Inanother embodiment, RF parameters for two or more cells are jointlyadjusted to maximize a network performance metric, e.g., QoE in terms ofcoverage, capacity, etc. Cell-specific RF configuration parameters maybe adjusted by prompting, or sending, the parameters to at least onecell.

In some embodiments, parameters are adjusted incrementally online.Parameters may be adjusted jointly for the different cells in a cluster,and the resultant feedback from UE measurement reports (MRs) may beobserved continually in a closed loop for long term optimization. RealUE feedback (e.g., no propagation model estimate) in MRs to update theobjective function, to identify cell state indicators, and to makestep-wise parameter adjustments. In some embodiments, the objectivefunction does not depend on UE location information.

As long as MRs (RSRP, RSSINR or RSRQ) from representative UEs areavailable for a given parameter change, the objective function can beevaluated accurately. As such, the objective function may not requirecorrect antenna tilt and power information. System objective functionsand cell level metrics may be aggregations of UE state information(e.g., MRs, etc.) that don't require individual UE location forevaluation. Even if initial configuration parameters are inaccurate,they can be still adjusted in a meaningful direction using the fact thatparameter changes lead to measurable changes in cell/system metrics.

Aspects of this disclosure provide adaptive simulated annealing (SA)techniques that combine online optimization of the real network viaclosed-loop SA-based guided random search and proactive offlineoptimization of relevant parameters and/or actions by efficientlyexploring the solution space via simulated networks (e.g., Netlab, Unet)iteratively, in order to, learn from experiences, such as mistakes andrewards. This may allow actions to be selected based on the real-timefeedback from the system. Embodiments may dynamically select and evolvethe best possible actions for online optimization, which may allow thesystem to adapt to new unforeseen conditions or situations. Embodimentsmay also update the models and parameters used by SA and/or simulatorsbased on online feedback from the system in real time, to provide fastconvergence and to escape the trap of local optimization.

In some embodiments, labels are assigned to the plurality of cells basedon the MRs and configuration parameters of the plurality of cells areadjusted according to the labels. In one embodiment, each of theplurality of cells are assigned two or more status labels based on oneor more MRs collected in the wireless network. The two or more statuslabels are associated with different cell status categories. In oneembodiment, a cell status may be categorized as a coverage status, aquality status, an overshooter status, or an interference status. Eachof the cell status categories may be further classified into differentcell status types. For example, a quality status is classified intotypes of {good, bad}, or an interference status is classified into typesof {strong, medium, weak}. A cell may be mapped to one of the cellstatus types corresponding to a cell status category based on MRs and islabeled by that type and category. A combination of the labels assignedto each of the cells in the wireless network reflects the current statusof each corresponding cell with respect to different cell statuscategories, and is used to determine adjustment of one or moreconfiguration parameters of each corresponding cell, for improving cellperformance. In one embodiment, domain expertise, knowledge andexperience are used to determine what actions to take to adjust thecells' configuration parameters based on the combinations of labels.

In some embodiments, blames are assigned to the plurality of cells basedon the MRs, and the configuration parameters of the plurality of cellsare adjusted according to the blames. Blames are associated with MRsthat do not satisfy a pre-defined set of performance criteria, which arereferred to as bad or unsatisfactory MRs, and indicate responsibilitiesthat one or more cells should take for the bad MRs. In one embodiment,bad MRs are identified from the collected MRs, and each bad MR isassociated with one unit of blame. For each bad MR identified in thewireless network, fractional units of blame are assigned to responsiblecells. If one cell is fully responsible for a bad MR, the cell isassigned a unit of blame. Thus the joint impacts of cell performanceissues, such as problems related to coverage, quality or interference,resulted from cell's configuration is captured into the blames assignedto the cell corresponding to bad MRs in the wireless network. Blamesassigned to each of the plurality of cells are used to determineadjustment of one or more configuration parameters of each correspondingcell, in order to improve status of each corresponding cell. In oneembodiment, domain expertise, knowledge and experience are used todetermine what actions to take to adjust the cells' configurationparameters based on the blames assigned to the cells.

In some embodiments, blames are classified into different blamecategories for determining configuration parameter adjustment of thecells. The different blame categories indicate different manners toadjust one or more configuration parameters of the cells in order toreduce the values of blames. In one embodiment, a blame is classifiedinto an up-blame or a down-blame, indicating an increase or a decreaseof a configuration parameter is needed in order to reduce the blamevalue. In one embodiment, blames assigned to each of the cells areclassified into an up-blame or a down-blame, and a sub-total up-blamevalue and a sub-total down-blame value are calculated by summing allup-blames and all down-blames, respectively, assigned to eachcorresponding cell. In one embodiment, the sub-total up-blame value andthe sub-total down-blame value of a cell are used to calculate anup-action probability and a down-action probability of the cell. Aconfiguration parameter of the cell may be increased when the up-actionprobability is greater than a first threshold, and may be decreased whenthe down-action probability is greater than a second threshold.

FIG. 6 shows a process 600 for optimizing performance in a network. Ingeneral, process 600 adjusts antenna configuration parametersincrementally online, jointly, and per cluster. Process 600 observes theresultant feedback from measurement reports (MRs) transmitted by UEdevices and continues in a closed loop to optimize over the long run.Antenna configuration parameters include electronic tilt, azimuth, andreference symbol power. Feedback from actual UE devices is used in theform of MRs, as opposed to propagation model estimates. As known in theart, the MRs can include multiple UE-related and cell-relatedparameters, such as cell ID, reference signal received power (RSRP),reference signal received quality (RSRQ), serving cell ID, and timingadvance parameters. The information in the MRs is used to update anobjective function representing network performance, identify cell stateindicator metrics/labels, and make step-wise antenna configurationparameter adjustments for performance progress. As known in the art, anobjective function can be used for optimization of a measurablequantity, parameter, or feature, such as network performance. As usedherein, the disclosed objective function can be used for optimization ofnetwork performance.

Process 600 does not need to know where UE devices are located within anetwork nor the exact antenna configuration parameter values in order tooptimize performance. This contrasts with propagation model aidedsolutions (such as ACP) that require accurate user location and correctantenna configuration parameter values for each cell. Because correctconfiguration parameter values are not known, even if initialconfiguration parameters are erroneous, the antenna configurationparameter values can still be adjusted in a meaningful direction due tothe fact that parameter changes lead to measurable change in cell/systemmetrics. As long as MRs (including RSRP, RS-SINR RSRQ, or the like) fromrepresentative UE devices (e.g., UE devices selected by unbiased randomsampling) are available for a given antenna configuration parameterchange, the objective function can be evaluated accurately.

In the disclosed embodiments, every MR that is adjudged to have “failed”a coverage criterion (e.g., by virtue of a reported reference channelsignal strength not meeting a pre-defined threshold) or a qualitycriterion (e.g., by virtue of a reported reference channel quality,i.e., signal to interference plus noise, not meeting another pre-definedthreshold) assigns a notional unit of “blame” for such failure to a“responsible” cell or cells. If multiple cells are held responsible,fractional units of “blame” (or “shares of blame”) are assigned to eachresponsible cell. When aggregated over all “failed” MRs, blame metricscan be calculated for each cell, and a base incremental action (e.g.,antenna tilt or transmit power adjustment) can be taken by the cell inaccordance with such blame metrics in order to reduce the rate ofoccurrence of MR failures.

Process 600 employs two closed loop phases—a base incremental adjustmentphase 605 and a biased random adjustment phase. In the base incrementaladjustment phase 605, cell level features or blame metrics arecalculated from the MRs and, alternatively or in addition, cells arelabeled according to a coverage, quality, interference, or overshooterstate (described in greater detail below with respect to FIGS. 4A-4E)that map to “intuitively correct” adjustment directions for the antennaconfiguration parameters based on domain knowledge appliedsimultaneously on multiple cells in order to quickly grab big initialgains. Embodiments for determining cell states are described in greaterdetail later in this disclosure. MRs are processed to derive cell levelmetrics accounting for every cell's share of blame for measurementreports indicating inadequate coverage or quality. The cell levelmetrics determine what base incremental adjustments are made to thatcell's antenna configuration parameters. Alternatively or in addition,MRs are processed to derive intuitive cell labels or combinations ofcell labels indicating any of coverage, quality, interference, andovershooter state of each cell. The one or more labels attached to acell determine the base incremental adjustments made to that cell'santenna configuration parameters.

The biased random adjustment phase represents a mathematical searchprocedure that performs explorative techniques and chooses oppositionalor random initial directions. Adjustments are accepted when theobjective function is improved and accepted with decreasing probabilityas the objective function worsens and with passage of time (cooling) tosteadily improve the solution. Over time, exploration direction can beconditioned to learn from mistakes and, in a later explorative pass, theaction learned to be best (in the sense of maximizing instantaneous orcumulative rewards) for a given cell state is chosen. The key factsbeing exploited are that the system objective function and cell levelmetrics are aggregations of UE state information (MR) that don't requireindividual UE locations for evaluation, and that parameter changesmatter but not the absolute value.

Process 600 begins at block 602 with the receipt of MRs from UE devices.Initiation of the optimization process is triggered at block 604.Optimization may be triggered manually, by network conditions, orautomatically based on key performance indicators (KPIs) within anetwork. Examples of KPIs include call drop rate and call block rate.Other KPIs are known to those of skill in the art. If analysis of KPIsidentify a degradation in network performance, then optimization istriggered. Upon triggering of optimization, process 600 proceeds to thebase incremental adjustment phase 605, which includes blocks 606 and608.

In the base incremental adjustment phase 605, MRs are used in block 606to determine a direction of adjustment to the antenna configurationparameters (i.e., whether to adjust an antenna configuration parameterup or down). Only the direction of change is determined and not thespecific current or starting values of the antenna configurationparameters. The direction of adjustment may be determined in severalways. In one example, the direction of change for each antennaconfiguration parameter is determined by a blame action metric where amajority rule of UE devices provide MRs indicating a certain change in adirection (up or down) for a respective parameter. In another example,each cell is labeled with a cell state based on the MRs received from UEdevices. A cell may be given one or more labels identifying a state ofthe cell, such as an interferer, non-interferer, good/weak coverage,good/weak quality, overshooter, and non-overshooter. Here, interferencerefers to downlink interference in the cell. These labels are typicallydetermined based on a comparison with one or more thresholds. The exactdetermination of these thresholds is beyond the scope of thisdisclosure. The labels given to a particular cell determine the changein direction for the antenna configuration parameters associated withthat particular cell.

After each change in the antenna configuration parameters of the cells,the objective function for network optimization is calculated uponreceiving new MRs in block 608 to determine if network performanceimproves. The objective function is based on a coverage parameter suchas RSRP and a quality parameter such as signal to interference and noiseratio of the reference signal (RS-SINR). The objective function isdetermined by identifying those MRs having their RSRP parameter greaterthan a first threshold value and identifying those MRs having theirRS-SINR parameter greater than a second threshold value. In someembodiments, the objective function is calculated according to theequation:k1*number of (RSRP>threshold1)+k2*number of (RS-SINR>threshold2),where k1 and k2 are non-negative numbers that sum to 1.0 and aredetermined in advance, e.g., by a system user (such as a networkengineer) or automatically in a configuration routine. As long asnetwork performance improves as indicated by an increase in theobjective function, process 600 will loop through the base incrementaladjustment phase 605 in blocks 606 and 608.

Upon identifying a decrease in the objective function in block 608, thebase incremental adjustment phase 605 ends and the biased randomadjustment phase 609 including blocks 610, 612, and 614 begins. In thebiased random adjustment phase 2609, simulated annealing is performedwhere random direction changes are made to the antenna configurationparameters and chaotic jumps are made to escape local minima positionsin order to steadily improve the objective function toward a globaloptimum level. The biased random direction changes are accepted uponobtaining an improvement in the objective function. If the objectivefunction decreases, a probability factor is used in determining whetherto accept the random direction changes. Table I shows an example of asimulated annealing algorithm.

TABLE I 1. Obtain initial solution S and position T 2. Determine C asthe cost of S 3. Generate new solution S′ 4. Determine C′ as the cost ofS′ 5. Accept S′ as the current solution S with probability p: p = exp[(C− C′)/T] if C′ ≥ C; p = 1 if C′ < C 6. If equilibrium level has not beenreached, go to 3. 7. Update position T 8. If termination criterion hasnot been reached, go to 3.

In terms of the present disclosure, biased random adjustments aredetermined and performed in block 610. After the biased randomadjustments have been made, new MRs are received and used to calculatethe objective function in block 612. A determination is made as towhether to accept or discard the adjustments based at least on therecalculated objective function in block 614. If the biased randomadjustments are discarded, alternative biased random adjustments may bedetermined when the process 600 returns to block 610. The biased randomadjustment phase continues to loop through blocks 610, 612, and 614 andfine tune the parameters until a convergence to a global maximum isreached.

FIG. 7 shows a graph 700 of how the antenna configuration parameters ofpower and downtilt affect network performance (as measured by theobjective function). The goal of process 600 is to identify a desiredoptimum network performance level 708 from a starting point 702. Process600 is not aware of the particular starting point 702. Iterating throughthe base incremental adjustment phase 605 will attain a firstintermediate network performance level 704. The biased random adjustmentphase will then kick in to perform chaotic jumps to identify the desiredoptimum network performance level 708, possibly through one or moresecond intermediate network performance levels 706.

As described above, an analytics assisted fully automatic closed loopself-organizing network provides a general framework for solving largescale near real time network optimization problems (SON use cases) Theoptimization process disclosed herein learns online the environment viareal-time feedback of UE MRs and cell KPIs using machine learninganalytics to assign actionable metrics/labels to cells. The optimizingprocess self-adapts internal algorithm parameters (like metricthresholds) to changing circumstances (data) and learns the correctaction rule for a given cell in a given state. Domain expertise andsophisticated processes (explorative and learning based optimization)are combined in phases for deciding joint corrective actions. Thisapproach contrasts to other approaches that use ad hoc engineeringknowledge based rules and unreliable models. The optimization process isrobust to engineering parameter database errors and lack of knowledge ofUE locations and has minimal modeling assumptions in contrast toexpensive and unreliable UE location based optimization techniques.

The optimization process is self-driving in that it uses machine learnedcell labels or blame metrics with engineering knowledge guided smallstep actions to extract quick initial gains in network performance. Forfurther optimization, action is taken in a biased random manner thatbalances reward with exploration risk. The optimization process learnsfrom mistakes or wrong decisions with time to eventually pick a bestaction for a given cell state. As a result, the overall process is fastand outperforms engineers fazed by multi-cellular complex interactions.The optimization process provides a cost effective solution by reducingthe need for an army of optimization engineers and expensive drivetesting and model calibration. The optimization process may be readilyextended to optimize additional CCO parameters like channel poweroffsets and CCO & Load Balancing (CCO+LB) scenarios. The optimizationprocess works for diverse scenarios, including adapting to changes inthe cellular network and traffic, and is readily transferable andscalable to other communication domains and deployments.

Determining Cell States to Adjust Antenna Configuration Parameters

The process for optimizing cell specific antenna configurationparameters described above can use various cell states to perform baseincremental adjustments. Discussed below are embodiments for determiningsuch cell states according to this disclosure.

FIG. 8 shows a process 800 for determining cell states to adjust antennaconfiguration parameters. Process 800 begins at block 802 where MRs arereceived over the network from UE devices. As described above, the MRscan include multiple UE-related and cell-related parameters, such ascell ID, reference signal received power (RSRP), reference signalreceived quality (RSRQ), serving cell ID, and timing advance parameters.Data extraction, filtering aggregation, and processing are performed onthe MRs at block 804 to obtain values associated with networkperformance. Values analyzed for network performance include referencesignal strength values such as RSRP used in a network, reference signalquality values, such as Reference Signal Signal-To-Interference-NoiseRatio (RS-SINR) or RSRQ for a network may also be included in theanalysis effort.

Though discussed in terms of a network, process 800 may be implementedin other network types including a Universal Mobile TelecommunicationsSystem (UMTS) network. The reference signal strength values in a UMTSnetwork can include a Received Signal Code Power (RSCP) or Energy perChip and Interference Level (Ec/Io). Other values derived from the MRsmay also be used in the cell state determinations. Though MR informationand especially periodic MR information offer the best sampling of thenetwork, other sources of network data may be used including, but notlimited to, channel quality indicator (CQI), key performance indicators(KPI), Performance Monitoring (PM) counters, and key quality indicator(KQI) metrics.

The values derived from MRs transmitted by UE devices are used toperform several cell state determinations for each cell in the network.A coverage state analysis is performed at block 806 to determine whetherthe cell provides good or weak coverage. An example of such a coveragestate analysis is described in detail below with respect to FIGS. 9-11B.An overshooting analysis is performed at block 808 to determine whetherthe cell is an overshooter or a non-overshooter. An interferenceanalysis is performed at block 810 to determine whether the cell is aninterferer or non-interferer. A quality analysis is performed at block812 to determine whether the cell is of good or bad quality. At block814, cell labels are identified from the cell state determinations andeach cell synthesized by combining the set of cell state labels assignedto the cell to create a cell signature.

The cell signature (i.e., the combination of cell labels) for each cellmay be used in block 816 to automatically perform adjustments to theantenna configuration parameters in order to optimize for coverage,quality, and capacity, making use of domain knowledge for actions. Forexample, a network component may instruct a cluster of cells to adjusttheir cell configuration parameters (e.g., their antenna tilts, transmitpower, or both) based on the cell signature assigned to each cell. As aparticular example, if a cell is labeled as “good” coverage and “bad”quality, the transmit power of the cell may be increased. In anotherexample, if a cell is labeled as “good” coverage and “strong”interference, the antenna tilt and/or transmit power of the cell may bedecreased. In some embodiments, a combination of labels assigned to eachcell and the current antenna tilt and/or RS power level of eachcorresponding cell are used to determine cell configuration adjustment.In the example where the cell is labeled as “good” coverage and “strong”interference, if the current antenna tilt level of the cell is “small”,then the antennal tilt of the cell may be decreased by a small amount,which is a pre-defined level of antenna tilt amount. In someembodiments, the network component may map a combination of the statuslabels assigned to a cell and the current antenna tilt and/or RS powerlevels of the cell to an action and assign the action to the cell. Anaction represents a change of one or more of a cell's configurationparameters, such as increase or decrease of the antenna tilt and/or RSpower of the cell. An action may be assigned based on domain knowledge,experience or expertise in consideration of status labels assigned to acell, current configuration of the cell, and other factors that mayaffect its cell status.

In some embodiments, instead of a network component controllingautomatic adjustments, the adjustments may be performedsemi-automatically by providing the cell signatures to fieldoptimization engineers to guide them in making adjustments to theantenna configuration parameters in the correct direction.

In addition, cells with similar signatures may be clustered in block 818to build KPI models for predictive analysis. In general, KPI predictivemodels are algorithms that identify which KPIs are likely to be a rootcause of a poor key quality indicator (KQI), such as packet loss rate.For example, in the context of Coverage Capacity Optimization (CCO),antenna uptilt may be increased when a poor KQI is associated with anRSRP level, as that would indicate the root cause is poor coverage,while antenna downtilt may be increased when a poor KQI is associatedwith interference, as that would indicate the root cause is poorcoverage. KPI predictive models for groups of similar cells can predictnetwork performance given predictors such as traffic and resourceconsumption variables. KPI predictive models may also predictgains/losses due to the application of a new feature on a given type orgroup of cells. KPI predictive models are built based on actualhistoric/field trial data and have demonstrated value for use in featurerecommendations, analysis, and improvement. Additional informationregarding KPI predictive models can be found in commonly-owned U.S.patent application Ser. No. 14/810,699 filed Jul. 28, 2015, the contentsof which are incorporated herein by reference. Cell labels andsignatures generated from MRs transmitted by UE devices offer a way ofgrouping like cells to pool data together in building more powerfulpredictive analytics models.

FIG. 9 shows the coverage states that can be assigned to a cell asdetermined in block 806 of FIG. 8. A cell may have a state of goodcoverage 902 or weak coverage 904. If a cell is considered in a weakcoverage state 904, the cell may be further assigned a weak edge state906 or a weak interior/insufficient state 908. A cell assigned a weakcoverage state 904 may also be assigned both a weak edge state 906 and aweak interior/insufficient state 908. In addition, it is possible that acell assigned a weak coverage state 904 may not be considered either ina weak edge state 906 or a weak interior/insufficient state 908. Theassignment of a cell to a weak coverage state 904, a weak edge state906, and/or a weak interior/insufficient state 908 is based on RSRPvalues in MRs transmitted by UE devices 104. Of course, the coveragestates 902-908 shown in FIG. 9 are merely one example. In otherembodiments, there may be additional, intermediate coverage states. Forexample, there may be one or more additional weak coverage states basedon ranges of RSRP values.

FIG. 10 shows an example of how a cell may be considered in a weak edgestate 906 and/or a weak interior/insufficient state 908. A cell in aweak edge state 906 has a certain number/percentage of UE devices thatit serves with corresponding RSRP values below a coverage threshold. Inaddition, a cell in weak edge state 906 has a certain number/percentageof UE devices that it serves with RSRP values associated with one ormore neighboring cells within a coverage reference range of an averageRSRP value for the cell. In this scenario, a UE device with a low RSRPvalue corresponding to the best serving cell coupled with a high enoughRSRP value associated with a neighboring cell is most likely locatednear the edge of coverage provided by the best serving cell.

To be considered in a weak interior/insufficient state 908, the cell hasa certain number/percentage of UE devices that are served by the cellRSRP values below a coverage threshold. In addition, these UE devices donot report a RSRP value associated with a neighboring cell that iswithin the coverage reference range. A UE device with a low RSRP valuefor the best serving cell coupled with no significant RSRP value for aneighboring cell is most likely located near the interior of the cell.

FIGS. 11A-11B show a process 1100 for determining a coverage state for acell. In FIG. 11A, process 1100 first performs individual analysis ofeach UE device best served by the cell and categorizes each UE device asone of good coverage or weak coverage. Those UE devices of weak coverageare further categorized as being of weak edge coverage or weakinterior/insufficient coverage. In FIG. 11B, process 1100 thenaggregates the categories of the UE devices, determines ratios of UEdevices belonging to the cell with weak coverage, and compares the ratioto thresholds in order to assign a coverage state to the cell.

In FIG. 11A, process 1100 begins at block 1102 with the receipt of MRsfrom UE devices. From the MRs, those UE devices best served by the cellare identified in block 1104. For each UE device, the RSRP value fromthe MR corresponding to the cell is compared to a coverage thresholdvalue in block 1106. If this RSRP value exceeds the coverage threshold,the UE device is assigned to a good coverage category at block 1108. Ifthis RSRP value does not exceed the coverage threshold value, the UEdevice is initially assigned to a weak coverage category at block 1110.At block 1112, the RSRP values associated with neighbor cells in the MRof the UE device are compared to a coverage offset threshold range. Ifat least one RSRP value associated with a neighbor cell is within thecoverage offset threshold range, the UE device is assigned to a weakedge category at block 1114. If there are no RSRP values associated withneighbor cells within the coverage offset threshold range, the UE deviceis assigned to a weak interior/insufficient category at block 1116.Unlike a cell that can be assigned to either, both, or neither of a weakedge state and a weak interior/insufficient state, a UE device of weakcoverage is categorized as only one of weak edge or weak interior.

In FIG. 11B, process 1100 continues at block 1122 with the aggregationof the categories for the UE devices determined in FIG. 11A. At block1124, a ratio of weak coverage UEs is determined from the aggregation.The ratio of weak coverage UEs is compared to a coverage ratio thresholdat block 1126. If the ratio of weak coverage UEs does not exceed acoverage ratio threshold, then the cell is assigned a good coveragestate at block 1128. If the ratio of weak coverage UEs does exceed thecoverage ratio threshold at block 1126, the ratios for weak edge UEs andweak interior/insufficient UEs are determined at block 1130. At block1132, the ratio of weak edge UEs is compared to an edge ratio threshold.If the ratio of weak edge UEs exceeds the edge ratio threshold, then thecell is assigned to a weak edge state at block 1134. In addition, theratio of weak interior/insufficient UEs is compared to an interior ratiothreshold at block 1136. If the ratio of weak interior/insufficient UEsexceeds the interior ratio threshold, then the cell is assigned to aweak interior/insufficient state at block 1138. If neither the ratios ofweak edge UEs nor weak interior/insufficient UEs exceed their respectiveratio thresholds, the cell is assigned a weak coverage state in block1140.

FIG. 12 shows the overshooting states that can be assigned to a cell asdetermined in block 808 of FIG. 8. A cell may be assigned an overshooterstate 1202 or a non-overshooter state 1204. A cell may be considered tobe in an overshooter state 1202 if its associated RSRP value in a MR ofa UE device served by a distant cell in another region ranks within acertain number of top RSRP values for the distant cell.

FIG. 13 shows an example of a cell in an overshooter state. UE devicelocated in and best served by cell x1 of Region X transmits a MR to eNBradio access node providing coverage for cell x1. Note that the exactlocation of UE device is unknown and does not need to be known. Theparameter values in the MR transmitted by UE device provide anindication that UE device is served by cell x1 which is all that isneeded for analysis purposes. The parameter values in the MR transmittedby UE device may indicate a potential overshooter cell. In this example,cell y4 of Region Y may potentially be in an overshooter state. Cell y4may be in an overshooter state if a RSRP value associated therewith isin a certain top number of reported RSRP values and/or within a certainthreshold of the RSRP value corresponding to cell x1. For example, a MRreport transmitted by UE device in cell x1 includes multiple RSRP valuesassociated with different cells. Table II shows a ranked list of the topsix RSRP values reported by UE device in its MR.

TABLE II RSRP Value Rank Cell 1 x1 (overshootee) 2 x2 3 x3 4 y4(overshooter) 5 x4 6 x5

Cell y4, being in Region Y, is relatively far away from cell x1 ascompared to the other cells in Region X. Typically, a cell that isrelatively far away would not tend to be ranked near the top of the RSRPvalue list. Thus, it would be typical for cell y4 to be ranked muchlower in Table II (e.g., at least below cells x4 and x5, which are muchnearer to cell x1). By being in the top six of RSRP values for UEdevice, cell y4 is a potential overshooter. In addition, a UE device isconsidered in an overlapped state if a pair of cells appears in the topk values of the RSRP value list determined from the transmitted MRand/or the difference between RSRP values is less than a certainthreshold. An example threshold value is 3 dB, though any thresholdvalue may be used as desired. Consideration of multiple overlapped UEdevices in an area or network is given to identify potentialovershooters as overshooters or not overshooters, which will now bedescribed.

FIG. 14 shows a graph 1400 depicting a relationship between overlappedUE devices and overshooting identification. Each point in graph 1400 isa cell pair where the distance between cells in a cell pair increasesalong the y-axis. Ideally, a larger inter site distance between cells ina cell pair should lead to less overlapped UE devices for the cell pair.A relatively high number of overlapped UE devices exist for cell pairx1,x2; cell pair x1,x3; and cell pair y3,y5, which is expected sincethere is a relatively short distance between the respective cells ofeach cell pair. A relatively low number of overlapped UE devices existfor cell pair x1,y1 and cell pair x1,y2 as there is a relatively largedistance between the cells of each cell pair.

Outlier cell pairs from the norm indicate an overshooter potential. Theoutlier cell pairs, such as cell pair x1,y4, have an abnormally highnumber of overlapped UE devices as compared to cell pairs of a similarinter site distance. Identification of an outlier cell pair indicatesthat at least one cell in the cell pair may be in an overshooter state.Thus, cells x1 and y4 are both overshooter candidates; however, it isnot clear just from looking at FIG. 14 if cell x1 is the overshooter andcell y4 is the overshootee, or if cell y4 is the overshooter and cell x1is the overshootee. To determine the overshooter among the overshootercandidates, the ranked RSRP value lists such as shown in Table II arealso considered. From Table II, it can be seen that cell y4 is acandidate for an overshooter state as its associated RSRP value is in anunexpected position in the RSRP value list of a UE device being servedby cell x1 in a different region than cell y4. However, an examinationof a similar RSRP value list of a UE device being served by cell y4 mayreveal that cell x1 is not in an unexpected position in the RSRP valuelist. For example, cell x1 may rank below all of the cells y1-y6 andrank among the cells x1-x6, as would be expected if cell x1 is not anovershooter. Thus, by examining RSRP values lists for UE devices servedby cell x1 and UE devices served by cell y4, it can be determined thatcell y4 is an overshooter and cell x1 is not an overshooter.

FIG. 15 shows a process for determining an overshooter state of a cell.Process 1500 begins at block 1502 with the receipt of MRs from UEdevices for each cell. From the MRs, those UE devices best served byeach cell are identified in block 1504. Cells are then paired up withevery other cell at block 1506 and an inter site distance and number ofoverlapping UE devices are computed for each cell pair. Inter sitedistance may be normalized by the median inter site distance of a cellwith its top neighbors. Normalization may be performed by dividing theinter site distance of a reference cell (such as x1) in the pair to itstop n closest tier neighbor cells. Normalization is performed tostandardize a picture across cells and create a global database of realworld or well simulated examples. Outlier cell pairs are then identifiedin block 1508. An outlier cell pair may have an abnormal number ofoverlapping UE devices in relation to the inter site distance betweenthe cells in the cell pair. For each outlier pair, the overshootercandidate cells are determined at block 1510. Then, in block 1512, theovershooter(s) among the overshooter candidates are determined byexamining ranked lists of RSRP values. For example, as described above,the overshooter cell will have its associated RSRP value near the top ofthe RSRP values of the other cell in the outlier cell pair. Theovershooter state is assigned to the overshooter cell in block 1514.

In accordance with another embodiment, an algorithm for determining anovershooter state will now be described. The algorithm uses quantitiestermed NO, Serving_Radius( ) and Planned_Radius( ) which are defined asfollows.

N(s) is the set of all neighbor cells in an “estimated” neighbor list ofa given serving cell s. The set N(s) can be inferred or estimated(either making use of cell azimuth information or without it) based oninformation extracted from one or more MRs. At a later point in thealgorithm, N(s) can also be used to calculate a feature normalizationfactor, which is the sum of all MRs served by cell s and its neighbors.

Serving_Radius(s,o) maps one or more topology parameters involving apair of cells (serving cell s and neighbor cell o) to a radius ofserving cell s in the direction of cell o.

Planned_Radius(s) of a cell s is the average or median ofServing_Radius(s,o) over a predetermined most-related subset of cells oin the neighbor list of s, i.e., all o in N(s).

The algorithm performs overshooter detection as follows. In one or morecell-level variables for cell c, a counter for the algorithm counts thefollowing values:

(1) The number of MRs served by a cell c with bad serving cell RSRQ(e.g., worse than T3 dB) and with no other significant overlapping cells(i.e., RSRPs in the MR list that are within T2 dB of the serving cell)that are “far away,” as determined by the TA distance from c. Here, T3is a predetermined RSRQ threshold separating good RSRQ of an MR (for theserving cell) from bad RSRQ and may be in a range of, e.g., [−20,0]. T2is a predetermined RSRP offset to determine whether a pair of cells havesignificant overlap in an MR and may be in a range of, e.g., [9, 20]. TAdistance is a parameter that is found in the MR and represents anestimated distance of a UE device that submits the MR from its servingcell.

(2) The number of MRs served by cell c with bad serving cell RSRQ (e.g.,worse than T3 dB) and other significant overlapping cells present thatare “far away” in terms of TA distance from c and such that the numberof significant “far away” non-neighbor overlapping cells form asignificant fraction (e.g., larger than Tn threshold) of the totalnumber of overlapping cells. Here, Tn represents a threshold of aproportion of neighbors to the total number of cells seen in an MR forovershooter detection. As this is a ratio of small integers, onlycertain quantized values (e.g., between 0 and 1) make sense as thresholdchoices.

(3) The number of MRs not served by cell c with bad serving cell RSRQ(e.g., worse than T3 dB) and in which cell c is a significant overlapperand also a “far away” non-neighbor of the serving cell (that itself hasbeen judged to be “not far away” from the MR).

This counter is then normalized with a blame normalization factor of c(i.e., the number of MRs served by c and all of its neighbors) andcompared with a threshold Tos. Here, Tos is a predetermined thresholdand may be between 0 and 1.

The cell c is declared an overshooter if the normalized overshootcounter of cell c exceeds Tos AND the fraction of MRs served by cell cwith respect to an analysis cluster average per cell exceedsTosormintraf. Here, Tosormintraf is a predetermined threshold thatrepresents a minimum fraction of traffic (i.e., served MRs of acell/analysis cluster average of MRs per cell) that a cell must carrybefore it is eligible to be declared as an overshooter. This lattercondition on cell c's traffic is for stable statistical inferencepurpose. It is noted that the “far away” judgment above for an MR isbased on its TA distance ratio (with respect to the serving cell'splanned radius) exceeding Factor1Upper. Here, Factor1Upper represents apredetermined threshold to compare the ratio of the TA based distance ofMR to a planned radius of the serving cell and decide whether MR is faraway.

Normalization of the counters using the total traffic (served MRs) ofthe serving cell s and its estimated neighbors N(s) is important toensure the setting of standard thresholds invariant to traffic or thespecific set of cells being analyzed.

Thresholds used for overshooting, such as Tos, can be learned by offlineanalysis of real field trial or market data. If labeled examples (bydomain expert engineers) of overshooters are used to guide thresholdsetting, it is called supervised learning; otherwise it is calledunsupervised learning (that looks at the groupings of the metrics andoutliers to determine thresholds). Similarly, if automatic algorithmslearn the thresholds, it is called machine learning.

FIG. 16 shows the interference states that can be assigned to a cell asdetermined in block 810 of FIG. 8. As shown in FIG. 16, a cell may beconsidered as being a strong/multi-interferer 1602, amedium/single-interferer 1604, or a weak/non-interferer 1606. Of course,this is merely one example. In other embodiments, there may beadditional, intermediate interferer states betweenstrong/multi-interferer 1602 and weak/non-interferer 1606 that representdiffering levels of interference. A first cell may be an interferingcell to a second cell if a RSRP associated with the first cell in a MRof a UE device best served by the second cell is within a thresholdrange of an average RSRP reported by UE devices best served by thesecond cell.

FIG. 17 shows a process for determining an interferer state of a cell.Process 1700 begins at block 1702 with the receipt of MRs from UEdevices for each cell. From the MRs, those UE devices best served byeach cell are identified in block 1704. At block 1706, a determinationis made in each cell if a RSRP associated with another cell is within atop k of RSRPs for the cell and/or within a reference range of anaverage RSRP in each cell. A cell having a RSRP within a top k of RSRPsfor another cell may be an interferer to that cell. In block 1708, UEdevices best served by each cell as having a RS-SINR below a qualitythreshold due to a RSRP of another cell being within a threshold rangeof top RSRP values for the cell are identified. An interference blamecounter is maintained in block 1710 for each cell as a cell pair withthe other cells to record how many UE devices are affected by anon-serving cell. A total blame counter for a cell is determined inblock 1712 by summing interference blame counters over all affectedcells. A check is made in block 1714 as to whether the total blamecounter is greater than a first or second interference threshold. If thetotal blame counter is not greater than the first or second interferencethreshold, the cell is assigned a weak/non-interfering state at block1716. If the total blame counter is greater than the first interferencethreshold but less than the second interference threshold, the cell isassigned a medium/single-interfering state at block 1718. If the totalblame counter is greater than the second interference threshold, thecell is assigned a strong/multi-interferer state. The total blamecounter may be normalized by the total number of UE devices served byall cells in the neighborhood of the cell being assigned an interfererstate.

The embodiment of FIG. 17 described above is based on consideration ofone interference feature or metric, namely the number of UE deviceshaving an RS-SINR below a quality threshold. This is merely one example.In other embodiments, other or additional interference features may beused in the analysis, including a number of cells a particular cellaffects significantly in terms of a number or percentage of affected UEdevices or an average or median RSRP of a potential interferer cell MRsof UE devise served by neighbor cells. In some embodiments, multipleinterference features may be considered against multiple correspondingthresholds. If multiple interference features are considered (each witha corresponding threshold), a clustering algorithm such as shown inFIGS. 3A-3E may be used to analyze the multiple interference featuresconcurrently.

FIG. 18 shows the quality states that can be assigned to a cell asdetermined in block 812 of FIG. 8. A cell may be considered as being ofgood quality 1802 or bad quality 1804. A particular cell with a certainpercentage of good quality UE devices where the particular cell is thebest server for the UE devices is assigned a good quality state. Aparticular cell with less than a certain percentage of good quality UEdevices where the particular cell is the best server for the UE devicesis assigned a bad quality state. A good quality UE device is one wherethe RS-SINR or RSRQ value is greater than a quality threshold value. Thequality threshold may be fixed, dynamically adjusted, or learned in asupervised, semi-supervised, or unsupervised manner by correlating UEdevice RS-SINR or RSRQ against relevant key performance indicators (KPI)and key quality indicators (KQI) describing a UE device quality ofexperience (QoE). Of course, the quality states 1802-1804 shown in FIG.18 are merely one example. In other embodiments, there may be one ormore additional, intermediate quality states between good quality 1802and bad quality 1804 based on intermediate thresholds of good quality UEdevices.

FIG. 19 shows a process 1900 for determining a quality state of a cell.Process 1900 begins at block 1902 with the receipt of MRs from UEdevices. From the MRs, those UE devices best served by the cell areidentified in block 1904. For the UE devices best served by the cell,the RS-SINR/RSRQ value from the MRs are compared to a quality thresholdvalue at block 1906. A percentage of UE devices best served by the cellthat exceed the quality threshold value is determined at block 1908. Atblock 1910, the percentage of UE devices exceeding the quality thresholdvalue is compared to a quality reference percentage. If the percentageof UE devices exceeding the quality threshold value is greater than thequality reference percentage, the cell is assigned a good quality stateat block 1912. If the percentage of UE devices exceeding the qualitythreshold value is not greater than the quality reference percentage,the cell is assigned a bad quality state at block 1914. The assignmentof a good or bad quality state to the cell affects the adjustments tothe antenna configuration parameters for the cell. The cell may beassigned a good or bad quality state in varying degrees based on howmuch the percentage is greater than or not greater than the qualityreference percentage. Differing degrees of good and bad quality statemay provide different adjustments to the antenna configurationparameters of the cell.

Solutions for Large Scale Near Real Time Network Optimization Problems

Embodiments of this disclosure provide a general approach for solvinglarge scale near real time network optimization problems (e.g., SON usecases). Embodiments of this disclosure may divide large networks intosubgroups of smaller networks, and then optimize control decisions forthe subgroups using a simulated annealing technique. Simulated annealing(SA) is a generic probabilistic meta-heuristic approach for solvingglobal optimization problems that locate a good approximation to theglobal optimum of a given function in a large search space. In anembodiment, a method may dynamically identify and/or sort problematiccells at the global or sub-group level, and optimize cells based onpriority such that the more problematic cells are optimized first. Insome embodiments, self learning solutions are executed online basedreal-time feedback (e.g., UE MRs, KPIs, mistakes, rewards). Selflearning solutions may also be executed offline based on a simulation.

Embodiments of this disclosure may provide techniques for avoiding localoptimization to obtain globally optimal, or near globally optimal,solutions. This can be achieved through simulated annealing (SA) basedguided random search via online learning from experience with the systemand proactive offline optimization via simulators, accepting worsesolution according to some criterions (e.g., Metropolis), etc.

Embodiments of this disclosure provide autonomous, closed-loop,adaptive, self-learning techniques that are robust across differentnetwork implementations. Embodiment approaches may utilize minimalmodeling assumptions, and may be insensitive to lack of UE locationinformation and/or inaccurate engineering parameters.

Control parameters for the cluster of cells may be adapted using anembodiment autonomous adaptive simulated annealing algorithm. Aspects ofthis disclosure provide autonomous adaptive simulated annealingalgorithms. An embodiment algorithm is described by the following tensteps.

The first step comprises obtaining an initial solution (S) and aninitial temperature (T0). In one embodiment, the starting temperature(T0) is selected based on an objective or cost function during anoffline simulation. In another embodiment, the starting temperature (T0)is selected by increasing the starting temperature (T0) until anacceptance ratio exceeds a threshold, e.g., ninety percent, etc.

The second step comprises evaluating the cost of the initial solutionusing constraints (e.g., thresholds and weights for parameters (e.g.,RSRP, SINR) used in objective function). This may include anormalization process that considers the cost per cell, the ratio oftotal cost to the total number of UEs, and the ratio of cost to numberof UEs per cell. The second step may also consider the cost per cell orper area (e.g., all cells or partial group of cells such as neighbors),cost percentage (e.g., ratio of cost per cell to UE number per cell),and distribution (e.g., weighted by cell).

The third step comprises generating a new solution (Snew). The newsolution may be generated using various adaptive (e.g., on-line)algorithm algorithms, including a uniform algorithm, a guided randomsearch (e.g., Gaussian, Cauchy). The new solution may also be generatedvia an offline simulation combined with reinforcement learning.Generating the new solution may include selecting which cell(s) are tobe adjusted. The cells may be chosen randomly, using a heuristicapproach, e.g., sorted by cost to UE no per cell, first m, exponentialprobability), or a using a hybrid approach (e.g., part random and partheuristic). The number of cells that are optimized may fixed (e.g., Xnumber of cells), or adaptive (e.g., based on the priority or severityof problematic cells). One or more parameters may be adjusted periteration. Various change/action/perturbation mechanisms may be appliedto adjust the parameters to be adjusted. For example, parameters may beadjusted in the positive or negative direction. The adjustments can usedifferent step size adjustment parameters, e.g., small step, large step,absolute step size, relative step size, fixed step-size/range, adaptivestep-size/range depending on the temperature at system/cell level oroffline simulation, etc.

The fourth step includes evaluating the cost of the new solution. Thefifth step includes determining whether to select the new solution asthe current solution. This decision may consider various criteria, andmay be probability-based and/or threshold based. For example, thedecision may consider criteria related to the cost of the new solution,e.g., difference between the cost of new solution and optimal cost, costper UE or per cell, etc.

The sixth step determines whether an equilibrium condition (# ofiterations carried out before update T) has not been reached. If not,then the technique reverts back to step three. The seventh stepcomprises learning from experience gained during the first six steps,e.g., feedback from the system, mistake, reward, etc. This step mayupdate models and/or parameters, such as control parameters (e.g.,system/cell level temperate Tn), propagation models used by simulators,engineering parameters, parameters/models for identifying problematiccells, generating new solution and accepting new solution, etc.

The eighth step determines whether a backward/safeguard condition hasbeen met. If so, the technique back-steps to a previous solutionaccording to some criteria. This step may be helpful in avoiding locallyoptimal solutions. The ninth step determines whether a terminationcriterion has been reached according to some criteria. If not, then thetechnique reverts back to step three. The tenth step returns allsolutions and relevant parameters, e.g., Sbest, Cbest, S, C, Sall andCall.

Aspects of this disclosure provide techniques for generating newsolutions for selected cells during SA-based self learning. FIG. 20illustrates an embodiment flowchart for generating new solutions forselected cells during SA-based self learning. As shown, the method 2000begins by starting a new round of optimization for a selected cell.Various criteria may be used to determine when to start a new round ofoptimization. In some embodiments, groups of two or more cells may beoptimized in parallel. In an embodiment, a new round of optimization maybe started only after a certain number of cells in the group havefinished the previous round of optimization. During the new round ofoptimization, a direction is selected for the cell. The possibledirections may include randomly generated and/or predefined directionsfor RF parameters, e.g., electronic antenna tilt, power (up/0, down/0,0/up, 0/down, 0/0), etc. The directions may be determined using adaptiveonline techniques, or via offline simulation. Various methods may beused to determine the direction, e.g., guided random, learning fromexperience (e.g., direction with maximum probability of positive gain),heuristic (e.g., expert system, whitebox), offline simulation (e.g.,Netlab), predefined order of directions, adaptive (e.g., up-tilt ifcurrent eTilt<(max−min)/2), reinforcement learning, etc.

Thereafter, parameter(s) are adjusted based on a step size in theselected direction, after which a solution is generated. Next, themethod 2000 determines whether to continue stepping in the currentdirection. If so, the parameters are adjusted once more in the selecteddirection, and a solution is generated. At some point, a determinationis made to change the direction for the current cell, at which pointparameters are adjusted in a different direction. Outputs are generatediteratively until a termination condition is reached, e.g., alldirections have been considered, a threshold number of directions havebeen considered, etc. Thereafter, a new cell is selected, and directionsfor the new cell are evaluated to generate corresponding solutions.Cells in the selected subset are evaluated iteratively until anothertermination condition is reached, at which point a new solution isoutput. Termination conditions may occur after performance of athreshold number of iterations or rounds. Termination conditions mayalso include results-based criteria, e.g., negative gain, number ofnegative gains, number of rejections, etc.

FIG. 21 illustrates a graph of the results of simulations of the method2000 described in FIG. 20. These results were obtained by starting a newround of adjustment without waiting for all cells to be adjusted in theprevious round. Three rounds of adjustment were performed. FIG. 22illustrates a graph of the results of simulations of the method 2000described in FIG. 20. These results were obtained by starting a newround of adjustment only after all cells had been adjusted in theprevious round.

Aspects of this disclosure provide techniques for dynamically adjustingcell-specific radio frequency (RF) configuration parameters (e.g.,electrical antenna tilt, reference symbol (RS) pilot power, etc.) tooptimize an objective function. In one embodiment, RF parameters of asingle cell are adjusted to maximize a per-cell performance metric. Inanother embodiment, RF parameters for two or more cells are jointlyadjusted to maximize a network performance metric, e.g., QoE in terms ofcoverage, capacity, etc.

In some embodiments, parameters are adjusted incrementally online.Parameters may be adjusted jointly for the different cells in a cluster,and the resultant feedback from UE measurement reports (MRs) may beobserved continually in a closed loop for long term optimization. RealUE feedback (e.g., no propagation model estimate) in MRs to update theobjective function, to identify cell state indicators, and to makestep-wise parameter adjustments. In some embodiments, the objectivefunction does not depend on UE location information.

As long as MRs (RSRP, RS-SINR or RSRQ) from representative UEs areavailable for a given parameter change, the objective function can beevaluated accurately. As such, the objective function may not requirecorrect antenna tilt and power information. System objective functionsand cell level metrics may be aggregations of UE state information(e.g., MRs, etc.) that don't require individual UE location forevaluation. Even if initial configuration parameters are inaccurate,they can be still adjusted in a meaningful direction using the fact thatparameter changes lead to measurable changes in cell/system metrics.

Aspects of this disclosure provide adaptive simulated annealing (SA)techniques that combine online optimization of the real network viaclosed-loop SA-based guided random search and proactive offlineoptimization of relevant parameters and/or actions by efficientlyexploring the solution space via simulated networks (e.g., Netlab, Unet)iteratively, in order to, learn from experiences, such as mistakes andrewards. This may allow actions to be selected based on the real-timefeedback from the system. Embodiments may dynamically select and evolvethe best possible actions for online optimization, which may allow thesystem to adapt to new unforeseen conditions or situations. Embodimentsmay also update the models and parameters used by SA and/or simulatorsbased on online feedback from the system in real time, to provide fastconvergence and to escape the trap of local optimization.

Aspects of this disclosure also provide embodiment SON optimizationtechniques that utilize an iterative learning approach to adjustwireless network configuration parameters. In particular, a controlleriteratively generates and evaluates global solutions over a sequence ofiterations. During this process, the controller uses experience obtainedfrom evaluating global solutions during previous iterations whengenerating global solutions in subsequent iterations. This may beachieved by using the evaluation results to update parameters (e.g.,topology model, traffic/usage patterns) of a heuristic/adaptivealgorithm used to generate the global solutions. In this way, thecontroller learns more about the network (e.g., topology, conditions,traffic patterns, etc.) during each successive iteration, whichultimately allows the controller to more closely tailor global solutionsto the network. As used herein, the term “global solution” refers to aset of local solutions for two or more wireless network coverage areasin a wireless network. Each “local solution” specifies one or morewireless configuration parameters for a particular wireless networkcoverage area. For example, in the context of CCO, a local solution mayspecify an antenna tilt of an access point in a wireless networkcoverage area and/or a transmit power level (e.g., uplink, downlink, orotherwise) for the wireless network coverage area. In some embodiments,the global solutions are evaluated during online implementation. Inother embodiments, the global solutions are evaluated during offlinesimulation. In yet other embodiments, some global solutions areevaluated offline while others are evaluated online. For example, thebest performing global solution obtained from a given number ofiterative simulations may be implemented during an online test period.Global solutions may be generated in a manner that seeks to improveperformance metrics of the worst performing cells. For example, wirelessconfiguration parameters for a global solution may be selected in orderimprove performance metrics in wireless coverage areas associated withthe highest costs.

Various techniques can be used to evaluate the global solutions. In someembodiments, each global solution is evaluated to determine whether itsatisfies one or more global performance criteria, e.g., an overallcost, an average per-cell cost, etc. If the global solution does notsatisfy the global performance criteria, then the controller may revertback to a previous global solution, e.g., a lowest cost global solutioncomputed during an earlier iteration. If the global solution doessatisfy the global performance criteria, then the controller mayevaluate each local solution specified by the global solution todetermine which local solutions satisfy corresponding local performancecriteria. Different local performance criteria may be used to evaluatelocal solutions for different coverage areas. Local solutions that failto satisfy their corresponding local performance criteria may bereplaced with previous local solutions, e.g., a default local solution,a local solution defined by a global solution computed in a previousiteration, etc. In some embodiments, the global performance criteria isa relative benchmark established during a previous iteration (e.g., thelowest cost global solution computed prior to the current globalsolution), while the local performance criteria is an absolutebenchmark, e.g., a minimum level of performance for a given cell.

In some embodiments, cost functions are used to evaluate globalsolution. The cost may be an overall cost for a set of coverage areas oran average per cell cost for a set of coverage areas. In the context ofcoverage and capacity optimization, a cost function for a globalsolution may include an RSRP parameter and an interference parameter,e.g., a SINR level, etc. In an embodiment, the RSRP componentcorresponds to a number of users reporting, or projected to report, anRSRP measurement below an RSRP threshold during a fixed period, and theinterference component corresponds to a number of users reporting, orprojected to report, an interference measurement above an interferencethreshold during the fixed period. In such an embodiment, the followingcost function may be used:Cost=0.5*Num_UE(RSRP≤Thr_rsrp)+0.5*Num_UE(INT≥thr_int), whereNum_UE(RSRP≤Thr_rsrp) is the number of UEs reporting, or projected toreport, RSRP levels below an RSRP threshold during a fixed period, andNum_UE(INT≥thr_int) is the number of UEs reporting, or projected toreport, interference levels below an interference threshold during thefixed period. In such an example, the interference levels may correspondto SINR levels obtained by measuring reference signals.

In some embodiments, some or all of the functions or processes of theone or more of the devices are implemented or supported by a computerprogram that is formed from computer readable program code and that isembodied in a computer readable medium. The phrase “code” includes anytype of computer code, including source code, object code, andexecutable code. The phrase “computer readable medium” includes any typeof medium capable of being accessed by a computer, such as read onlymemory (ROM), random access memory (RAM), a hard disk drive, a compactdisc (CD), a digital video disc (DVD), or any other type of memory. Uponexecution, the computer program may detect core traces, convert the coretraces into a hierarchical format, generate the gene function database,and determine preemption costs associated with the gene functions.

Adjusting Cell Configuration Parameters Based on Measurement Reports

Aspects of the present disclosure provide methods and apparatus foradjusting configuration parameters of a plurality of cells in a wirelessnetwork based on measurement reports (MRs) received during a datacollection period of the wireless network, so that coverage and capacityof the wireless network may be improved. A configuration parameter of acell may be an antenna tilt or a transmit power.

In some embodiments, labels are assigned to the plurality of cells basedon the MRs and configuration parameters of the plurality of cells areadjusted according to the labels. In one embodiment, each of theplurality of cells are assigned two or more labels based on one or moreMRs collected in the wireless network. The two or more status labels areassociated with different cell status categories. In one embodiment, acell status may be categorized as a coverage status, a quality status,an overshooter status, or an interference status. Each of the cellstatus categories may be further classified into different cell statustypes. For example, a quality status is classified into types of {good,bad}, or an interference status is classified into types of {strong,medium, weak}. A cell may be mapped to one of the cell status typescorresponding to a cell status category based on MRs and is labeled bythat type and category. A combination of the labels assigned to each ofthe cells in the wireless network reflects the current status of eachcorresponding cell with respect to different cell status categories, andis used to determine adjustment of one or more configuration parametersof each corresponding cell, for improving cell performance. In oneembodiment, domain expertise, knowledge and experience are used todetermine what actions to take to adjust the cells' configurationparameters based on the combinations of labels.

In some embodiments, blames are assigned to the plurality of cells basedon the MRs, and configuration parameters of the plurality of cells areadjusted according to the blames. Blames are associated with MRs that donot satisfy a pre-defined set of performance criteria, which arereferred to as bad or unsatisfactory MRs, and indicate responsibilitiesthat one or more cells should take for the bad MRs. In one embodiment,bad MRs are identified from the collected MRs, and each bad MR isassociated with one unit of blame. For each bad MR identified in thewireless network, fractional units of blame are assigned to responsiblecells. If one cell is fully responsible for a bad MR, the cell isassigned a unit of blame. Thus the joint impacts of cell performanceissues, such as problems related to coverage, quality or interference,resulted from cell's configuration is captured into the blames assignedto the cell corresponding to bad MRs in the wireless network. Blamesassigned to each of the plurality of cells are used to determineadjustment of one or more configuration parameters of each correspondingcell, in order to improve status of each corresponding cell. In oneembodiment, domain expertise, knowledge and experience are used todetermine what actions to take to adjust the cells' configurationparameters based on the blames assigned to the cells.

In some embodiments, blames are classified into different blamecategories for determining configuration parameter adjustment of thecells. The different blame categories indicate different manners toadjust one or more configuration parameters of the cells in order toreduce the values of blames. In one embodiment, a blame is classifiedinto an up-blame or a down-blame, indicating an increase or a decreaseof a configuration parameter is needed in order to reduce the blamevalue. In one embodiment, blames assigned to each of the cells areclassified into an up-blame or a down-blame, and a sub-total up-blamevalue and a sub-total down-blame value are calculated by summing allup-blames and all down-blames, respectively, assigned to eachcorresponding cell. In one embodiment, the sub-total up-blame value andthe sub-total down-blame value of a cell are used to calculate anup-action probability and a down-action probability of the cell. Aconfiguration parameter of the cell may be increased when the up-actionprobability is greater than a first threshold, and may be decreased whenthe down-action probability is greater than a second threshold.

Conventional SON methods for CCO, such as the automatic cell planner(ACP), typically require costly drive tests and human verification toconfigure RF configuration parameters of a cell. For example, drive test(MT) or minimization of drive test (MDT) data, along with user equipment(UE) geo-location (AGPS) and accurate antenna configuration parametersare required to achieve an accurate propagation modeling based on whichcell configuration parameters are adjusted. Additionally, these methodsalso require significant manual effort to be applied for configuringdifferent types of cells, which results in high expenditure andcomplicated configuration process.

Aspects of the present disclosure provide methods and apparatus togenerally optimize a cluster of cells in terms of coverage and capacity,by utilizing measurement reports (MRs) obtained from UEs served by thecells and experts' domain knowledge to determine configuration parameteradjustment of the cells. Embodiments of the present disclosure do notreply on UE AGPS and do not require accurate antenna configurationparameters of the cells.

A measurement report generally includes measurement results that a UEmeasures and provides for delivery to its serving cell regarding variousmeasurement items the serving cell requests. For example, a measurementreport includes measurement results about signal strength or quality ofthe serving cell. Typically, a measurement report includes a referencesignal received power (RSRP) and a reference signal receive quality(RSRQ). A RSRP generally provides information about strength of areceived reference signal, and a RSRQ indicates quality of a receivedreference signal. Measurement and calculation of a RSRQ may be based ona RSRP and a received signal strength indicator (RSSI). A RSSI includesinformation about a reference signal power from a serving cell of a UEas well as co-channel interference and noise, and can help indetermining interference and noise information. As used herein, MRs sentby UEs served by a cell is referred to as MRs of the cell. Embodimentsof this disclosure use RSRP and RSRQ reported to indicate referencesignal strength and reference signal quality of a cell, respectively.However, the use of RSRP and RSRQ are merely for illustrative purpose,and any other measures for reference signal strength and referencesignal quality of a cell may also be used. For example, a signal tointerference and noise ratio (SINR) may be used to indicate referencesignal quality of a cell. One of ordinary skill in the art wouldrecognize many variations and alternatives of measures for referencesignal strength and reference signal quality of a cell. These variationsand alternatives are all within the scope of this disclosure withoutdeparting from the spirit of this disclosure.

A cell may be characterized by its cell status in different categories.For examples, a cell status may be a coverage status, a quality status,an interference status, or an overshooter status, etc. In someembodiments, cell statuses may be estimated or indicated based oninformation included in MRs. For example, a RSRP, or any other measurefor reference signal strength, may be used to indicate coverage statusof a cell at its edge, and a RSRQ, or an SINR, or any other measure forreference signal quality, may be used to indicate quality status withina cell coverage area. In some embodiments, a cell status in a categorymay be further classified into different status types. Status typescorresponding to a cell status category may be, as an example,represented by {type 1, type 2 . . . type n}. For example, a coveragestatus is classified into types of {good, weak, weak edge only, weakinterior/insufficient only, weak edge and interior/insufficient}. Insome embodiments, a cell's coverage status is “good” when an averageRSRP included in MRs of the cell is greater than a first threshold, andthe cell's coverage status is “weak” when the average RSRP is less thana second threshold. A coverage status type of “weak interior only” mayindicate that signal strength within a cell is less than a threshold,and “insufficient only” may indicate there is a gap between two cellsand UEs in the gap are not sufficiently covered. A coverage status typeof “weak edge and interior” may indicate signal strength within and atthe edge of the cell is less than a threshold. In an example, a qualitystatus may include types of {good quality, bad quality}.

In another example, an overshooter status is classified into two types:{yes (i.e., with overshooter), no (i.e., without overshooter)}. Inanother example, an interference status may be categorized into types of{interferer zero, interferer one, interferer more}, or {none, single,multiple} depending on the number of interferee cells (e.g., victimcells of an interference) affected. Alternatively, an interferencestatus of a cell, which is identified as an interferer, may haveinterference status types of {strong interferer, medium interferer, weakinterferer} based on the number of UEs/MRs which are affected by theinterferer and/or the number of interferee cells. One of ordinary skillin the art would recognize many variations and alternatives forclassifying cell status categories and for classifying a cell statusinto different types corresponding to a category. The terms of “label”and “status label” are used interchangeably throughout this disclosure.

A cell may be mapped to one of the status types corresponding to a cellstatus category utilizing information in MRs, and is labeled by thattype and category. For example, a cell may be assigned a label of “weak”corresponding to its coverage status, and/or be assigned a label of“yes” corresponding to its overshooter status based on RSRP informationincluded in MRs obtained. In one embodiment, a label assigned to a cellmay be referred to as a problematic label for it indicates aperformance, e.g., cell capacity or coverage, problem of the cell. Forexample, a label of “bad” quality status indicates there may be aquality issue in the cell, and a label of “strong” interference statusindicates that a cell may cause interference problems to other cells.Thus labels assigned to cells provide information indicating problems ofcells and also guides to adjust the cells' configurations. In someembodiments, labels assigned to a cell are used to determine whether andhow the cell's configuration parameters are adjusted, in expectation ofimproving one or more labels of the cell, and consequently improving thecell performance and MRs received in future.

FIG. 23 illustrates a flowchart of an embodiment method 2300 forimproving coverage and capacity of a cluster of cells in a wirelessnetwork. The method 2300 may be performed by a network component, suchas a communications controller or an evolved node B (eNodeB). The method2300 starts at step 2302, where the network component receives one ormore MRs. The MRs may be sent by one or more UEs served in the wirelessnetwork by one or more of the cluster of cells. The UEs may send the MRsperiodically (e.g., whenever pilots are received), when and asconfigured for pilot measurement reporting, or whenever requested. Inone embodiment, the network component also collects MDT and/or DT datain the wireless network.

At step 2304, each of the cluster of cells is assigned one or morestatus labels associated with different cell status categories based onthe MRs, and optionally, the MDT/DT data. For example, each cell isassigned two labels corresponding to the coverage status andinterference status. In another example, each cell is assigned fourlabels corresponding to the coverage status, the quality status, theinterference status, and the overshooter status. What cell statuscategory will be used to label the cells may depend on many factors,such as user experience, system load, number of user equipments, orimpacting problems to be solved in the network. Since each cell statuscategory may include different types, there may be various combinationof labels assigned to a cell corresponding to the cell status categoriesused. For example, a cell may be labeled as “good” coverage, “bad”quality, and “no” overshooter. Alternatively, the cell may be labeled as“weak” coverage, “good” quality and “no” overshooter.

At step 2306, the network component estimates the current antenna tiltand/or RS power for each cell in the wireless network. In oneembodiment, the current antenna tilt or RS power of a cell may berepresented by: (original antenna tilt/RS power+change value). Theoriginal antenna tilt/RS power represents the antenna tilt value/RSpower value of the cell at a point of time when the cell's configurationis set as original, and the change value represents increase or decreaseof the cell's tilt or RS power with respect to its original value. Inone example, the original values of each cell's antenna tilt and RSpower and their change values over the time may be stored in a database.Thus a current value of a cell's antenna tilt or RS power may beobtained by adding the original value and a previous change value. Inone embodiment, the estimation of the current antenna tilt or RS powerfor a cell may indicate a level of a value compared with the cell'soriginal antenna tilt or RS power. For example, a cell's current antennatilt may be estimated as “small”, which indicates that the currentantenna tilt is small compared with the original antenna tilt. Inanother example, a cell's current RS power may be estimated as “large”indicating the current RS power is large compared with the original RSpower. In one embodiment, the level of a tilt/RS power value may beclassified as “large”, “moderate”, “small”, and “zero” which indicatesthere is no change. A person of ordinary skill in the art wouldrecognize many variations for classifying the levels. In one embodiment,a cell's estimated antenna tilt and RS power may be represented by avector: [antenna tilt level, RS power level]. For example, a cell'sestimated antenna tilt and RS power may be [small, 0], [large,moderate], etc. The antenna tilt and RS power may also be taken intoconsideration when determining cell configuration parameter adjustment.For example, when a cell is assigned a label of “bad” quality andincrease of transmit power of the cell is desired. However, if thecurrent RS power is already “large”, the transmit power of the cell maynot be adjusted.

At step 2308, the network component instructs the cluster of cells toadjust their cell configuration parameters, such as their antenna tilts,transmit power, or both, based on status labels assigned to the cell. Inone embodiment, a combination of labels assigned to each cell may beused to determine cell configuration parameter adjustment. For example,if a cell is labeled as “good” coverage, and “bad” quality, the transmitpower of the cell may be increased. In another example, if a cell islabeled as “good” coverage and “strong” interference, the antenna tiltand/or transmit power of the cell may be decreased. In anotherembodiment, a combination of labels assigned to each cell and thecurrent antenna tilt and/or RS power level of each corresponding cellare used to determine cell configuration adjustment. In the examplewhere the cell is labeled as “good” coverage and “strong” interference,if the current antenna tilt level of the cell is “small”, then theantennal tilt of the cell may be decreased by a small amount, which is apre-defined level of antenna tilt amount. Alternatively, the antennaltilt of the cell may not be adjusted, and the transmit power of the cellmay be decreased according to its current RS power level. In oneembodiment, the network component may map a combination of the statuslabels assigned to a cell and the current antenna tilt and/or RS powerlevels of the cell to an action and assign the action to the cell. Anaction represents a change of one or more of a cell's configurationparameter, such as increase or decrease of the antenna tilt and/or RSpower of the cell. An action may be assigned based on domain knowledge,experience or expertise in consideration of status labels assigned to acell, current configuration of the cell, and other factors that mayaffect its cell status. Steps of 2302-2308 of the method 2300 may beperformed iteratively, with each cell's configuration parameter(s)adjusted in multiple “small steps”, for improving labels assigned to thecells.

In some embodiments, tables are used to indicate status labels andactions assigned to cells. FIGS. 24A-24C and FIGS. 25A-25C illustrateexamples of tables for mapping status labels to actions for cells in awireless network. Tables in FIGS. 24A-24C and FIGS. 25A-25C show statuslabels assigned to the cells, current antenna tilt and current RS powerlevels of the cells, and actions assigned to the cells for adjustingantenna tilt, RS power, or both. Actions for a cell are determined basedon a combination of labels assigned to the cell and the cell's currentantenna tilt and RS power levels. Each row represents labels and actionsassigned to a cell. As shown, the cells are listed according to aparticular problem, shown in the first column of “Case Name”, observedbased on MRs. That is, the “Case Name” column indicates a problematiclabel assigned to the cells corresponding to a cell status category. Forexample, the “Case Name” column of overshooter indicates these cellshave an overshooter problem and their labels corresponding to theovershooter status are “Yes”. In another example, the “Case Name” columnof multi-interferer indicates that these cells have been identified asinterferers of multiple interferees and have been assigned a label“multi-interferer” according to the interference status. The second tothe fifth columns show labels assigned to each of the cellscorresponding to the cover status, quality status, interference statusand overshooter status, and the sixth and seventh columns are currentantenna tilt and RS power levels estimated of the cells. Each of thecells is assigned an action to adjust its antenna tilt and/or RS power,as shown in the eighth and ninth columns.

Embodiment methods map UE level MR data to various disparate cell levellabels, and further to actions for adjusting cell configurationparameters, resulting in non-linear mappings, since actions aredetermined based on various combinations of labels. The methods may alsobe error prone, because labels could be incorrectly assigned due tomapping quantization errors, e.g., a “weak” coverage cell may beassigned a “weak edge only” label. Actions determined based on thelabels may also be wrong due to, e.g., poor mapping decisions and/oroverall quantization errors. Further, a so called “tricky or ambiguous”combination of labels may be assigned to a cell, which makes it hard todetermine an action. For example, when a cell is assigned labels of“multi-interferer” and “poor quality”, it is hard to determine whetherthe cell's antenna tilt or RS power should be increased or decreased,and consequently hard to map an action to the combination of labels.Moreover, the methods may not quantitatively inform about how many UEsin the system are affected due to a cell's “multi-interferer” status, aswell as how many “poor quality” statuses can be weighed against eachother for choosing a specific action. Additionally, the methods do notprovide ability to weigh a cell's “own” quality/coverage label againstinterference/overshoot labels of “other cells”, and to estimate impacton stability of a wireless communications network when multiple cellsinteract with each other due to actions taken which are determined basedon labels.

In some embodiments, a concept of “blame” is provided to indicate aresponsibility a cell may take for each MR that does not satisfy aperformance criterion of a wireless communications network. Such a MRmay be referred to as a “bad” or “unsatisfactory” MR. In someembodiments, the system may pre-define a set of performance criteria. Ifa MR does not satisfy one of the set of criteria, the MR is marked as abad or unsatisfactory MR; otherwise, it is a good MR. For example, theset of performance criteria includes a coverage criterion, e.g., havinga RSRP, or any other measure for reference signal strength, greater thana first threshold, and a quality criterion, e.g., having a RSRQ, or anyother measure for reference signal quality, greater than a secondthreshold.

When a RSRP included in a MR is not greater than the first threshold,the MR does not satisfy the coverage criterion and is bad, or when aRSRQ in the MR is not greater than the second threshold, the MR is bad.A person of ordinary skill in the art would recognize many variationsand alternatives for defining the performance criteria by which MRs maybe identified as good or bad. For each bad MR, there should be one ormore cells responsible for such an MR and thus taking the blame for it,and a blame is assigned to a cell if the cell takes at least a partialresponsibility for a bad MR. Each assigned blame may be associated witha blame value. In one embodiment, each bad MR is associated with oneunit of blame. If one cell is fully responsible for the bad MR, this onecell takes one unit of blame. If multiple cells are responsible for thebad MR, these multiple cells share the one unit of blame. The cells mayinclude a serving cell of the UE reporting the bad MR, a non-co-siteneighbor cell of the serving cell, or other cells in the system.

FIG. 26 illustrates a diagram of an embodiment wireless communicationsnetwork 2600 where blames are assigned to cells. As shown, the network2600 includes cells 2610, 2620 and 2630 serving UEs in their respectivecoverage areas. Mobile devices 2612, 2614 and 2616 are within thecoverage area of the cell 2610 and transmit MRs to the cell 2610. Inthis example, each of the mobile devices 2612 and 2614 reports a bad MR2622 and a bad MR 2624, respectively, and the mobile device 2616 reportsa good MR 2626. Each bad MR is associated with a unit of blame. Byanalysis of the MRs 2622-2626, the cell 2610 is fully responsible forthe bad MR 2622, and one unit of blame (e.g., a blame value of “1”) isassigned to the cell 2610. The cells 2620 and 2630 are not assigned anyblame for the bad MR 2622 since they do not take any responsibility forit.

In one embodiment, the cells 2620 and 2630 may also be assigned blamesfor the bad MR 2622, with each having a blame value of “0”. Cells 2610,2620 and 2630 are responsible for the bad MR 2624 reported by the mobiledevice 2614, and share the responsibility equally, so each of the cells2610, 2620 and 2630 are assigned ⅓ unit of blame. In another word, eachof the cells 2610, 2620 and 2630 is assigned a blame with a value of“⅓”. This may be the case when cells 2620 and 2630 cause interference onthe mobile device 2614, while the cell 2610 has a low transmit power.The mobile device 2616's good MR 2626 is good and thus does not imposeblames on any of the cells. In this example, the total blame valueassigned to all the cells in the wireless communications system equalsthe number of bad MRs received, that is, (1+⅓+⅓+⅓)=2 (i.e., two bad MRs2622 and 2624).

By using the concept of blame, responsibilities of a cell for causingbad MRs in the wireless communications network are captured andidentified, based on which corresponding adjustment to the cell'sconfiguration parameters may be determined in order to reduce the blamevalues of the cell and consequently the number of bad MRs, thus theentire network performance is improved. FIG. 27 illustrates a flowchartof an embodiment method 2700 for adjusting cell configuration parametersin a wireless network based on blames. The method 2700 may be performedby a network component in the wireless network, such as a communicationscontroller, or an eNodeB. At step 2702, the method 2700 identifies badMRs, i.e., MRs failing to satisfy one of a set of performance criteria,from a plurality of MRs received in the wireless network, e.g., during adata collection time period. The plurality of MRs are measured andreported by UEs served by multiple cells in the wireless network, andmay be transmitted by the UEs periodically or upon request. Each of thebad MR is associated with one unit of blame.

At step 2704, for each of the identified bad MRs, the method 2700assigns fractional units of blame to responsible cells. Thus for each ofthe bad MRs, a cell is assigned a blame associated with a blame value.The blame value may be “1” when the cell is fully responsible for thecorresponding bad MR, may be “0” when the cell is not responsible forthe corresponding bad MR, and may be between 0 and 1 when the cell ispartially responsible for the corresponding bad MR. A blamecorresponding to a bad MR may be assigned to a cell based on informationincluded in the bad MR, such as a RSRP list, RSRQ and timing advance,topology information of the cell, and other information collected, suchas MDT data. At step 2706, the method 2700 instructs one or more of thecells to adjust one or more of their configuration parameters based ontheir assigned blame values. Example configuration parameters include anantenna tilt, and a power parameter, such as a transmit power, etc. Inone embodiment, blames assigned to a cell may be used to label the cellcorresponding to different cell status categories, such as a coveragestatus or a quality status, taking into consideration of information,e.g., included in MRs received in the cell. The cell's configurationparameter may then be adjusted based on labels assigned to the cell, asillustrated in FIG. 30.

In some embodiments, a blame assigned to a cell according to a bad MRmay be classified into different blame categories. The blame categoriesare associated with different manners for adjusting one or more cellconfiguration parameters in expectation of reducing the value of theblame and thus the number of bad MRs. In some embodiment, a blame may beclassified into an up-blame or a down-blame. An up-blame or a down-blameindicates increase or decrease of one or more cell configurationparameters is desired for reducing the value of the up-blame. So theup-blame and down-blame corresponds to an increase-action (or up-action)and a decrease-action (or down-action), respectively, for adjusting oneor more cell configuration parameters. In one example, a blame assignedto a cell is classified as an up-blame if increasing the antenna tilt,transmit power, or both of the cell is expected to reduce the associatedblame value, and the blame is classified as a down-blame if decreasingthe antenna tilt, transmit power, or both of the cell is expected toreduce the cell's blame values. For example, a cell may be assigned adown-blame with a value “⅕” or an up-blame with a value “1”. In anotherexample, a blame may be classified into three categories, where thefirst category indicates increase of both antenna tilt and transmitpower are desired, the second category indicates decrease of bothantenna tilt and transmit power are desired, and the third categoryindicates increase/decrease of an antenna tilt and decrease/increase ofa transmit power. A person of ordinary skill in the art would recognizemany variations and alternatives for defining the blame categories andadjusting a cell's configuration parameters.

Classifying blames assigned to cells into different categories ishelpful in determining how configuration parameters of the cells may beadjusted based on blames. FIG. 28 illustrates a flowchart of anembodiment method 2800 for adjusting cell configuration parameters in awireless network having multiple cells based on blames. At step 2802,the method 2800 assigns blames to responsible cells for each bad MRidentified in the wireless network. Each bad MR is associated with oneunit of blame, and responsible cells are assigned fractional units ofblame for each bad MR. At step 2804, each of the assigned blames isclassified into a blame category of multiple blame categories. Forexample, a blame is classified into either an up-blame or a down-blamed,as described above. Steps 2802 and 2804 are repeated for all bad MRsreceived during a data collection period of the wireless network. Thusresponsibilities, i.e., blames for all bad MRs are assigned.

At step 2806, the method 2800 calculates, for each cell, a sub-totalblame corresponding to each of the blame categories. In someembodiments, the method 2800 sums the fractional units of blame that areassigned to each cell and that fall into a corresponding blame category,by which a sub-total blame value of the corresponding blame category isobtained. Taking the up-blame and down-blame categories as an example, asub-total up-blame value for a cell is calculated by summing allup-blame values assigned to the cell, and a sub-total down-blame valuefor the cell is also calculated by summing all down-blame valuesassigned to the cell. Thus for each of the cells, two sub-total blamevalues are calculated, which include a sub-total down-blame value and asub-total up-blame value.

At step 2808, the method instructs one or more of the cells to adjusttheir configuration parameters based on the sub-total blame values indifferent blame categories. In some embodiments, action probabilitiesmay be calculated and used to determine how a cell's configurationparameters are adjusted. For example, when a sub-total down-blame valueand a sub-total up-blame value are obtained for each of the cells, anup-action probability P_(up-action) and a down-action probabilityP_(down-action) may be calculated for each cell as follows:P _(up-action)=(sub-total up-blame value)/(total blame value)P_(down-action)=1−P _(up-action)

where the total blame value equals (sub-total down-blame value+sub-totalup-blame value), the up-action probability indicates a probability of aneed for a cell to increase one or more of its configuration parametersfor reducing its blames (responsibilities) for bad MRs, and thedown-action probability indicates a probability of a need for the cellto decrease its configuration parameters in order to reduce itsresponsibilities for bad MRs. In one embodiment, the up-actionprobability and the down-action probability are compared with apre-defined up-action threshold TP_(up) and a pre-defined down-actionthreshold TP_(down), respectively, to determine actions to be taken toadjust a cell's configuration parameters. For example, if the up-actionprobability of a cell is greater than the TP_(up), a configurationparameter of the cell, such as the antenna tilt or transmit power of thecell, may be increased. If the down-action probability of a cell isgreater than the TP_(down), a configuration parameter of the cell, maybe decreased. Generally, the pre-defined up-action threshold TP_(up) andthe pre-defined down-action threshold TP_(down) should be greater thanor equal to 0.5. As such a cell is only eligible for adjusting itsconfiguration parameters by either increasing or decreasing theconfiguration parameters. If neither of the up-action probability andthe down-action probability of a cell is greater than the correspondingthreshold TP_(up) or TP_(down), then no action will be taken to adjustthe cell's configuration parameters.

FIG. 29 illustrates a graph 2900 showing an embodiment up-blame anddown-blame space, where analysis may be performed to determine actionsfor adjusting a cell's configuration parameters. The horizontal axis ofthe graph 2900 represents a normalized up-blame and the vertical axisrepresents a normalized down-blame. Each point in the graph represents acell. As shown, the up-blame and down-blame space is divided into zones2910, 2920, 2930 and 2940. Zone 2910 is a down-action zone, indicatingcells in this zone have a down-action probability greater than apre-defined down-action threshold TP_(down). These cells generally havea high confidence to perform a down-action, i.e., decreasing aconfiguration parameter of the cells, with expectation of reducing theirblame values. Zone 2920 is an up-action zone, indicating cells in thiszone have an up-action probability greater than a pre-defined up-actionthreshold TP_(up). Similarly, these cells also have a generally highconfidence to perform an up-action to increase their configurationparameters. Zone 2930 is a no-action zone, where cells in this zone donot adjust their configuration parameters, since neither theirdown-action probabilities nor up-action probabilities are greater thanthe pre-defined thresholds. Cells in zone 2930 may have low confidenceto determine an action for adjusting their parameters. Zone 2940 is agood zone, which indicates cells in this zone do not need to adjusttheir configuration parameters. This may be the case when cells in thezone 2940 are not responsible for any bad MRs, or when the cells havevery small up-blame and down-blame values, even if their down-actionprobabilities or up-action probabilities exceed the correspondingpre-defined threshold. Cells on line 2912 are those having a down-actionprobability equal to the pre-defined down-action threshold TP_(down).Cells on the line 2916 have an up-action probability equal to thepre-defined up-action threshold TP_(up). Cells on line 2912 and line2916 may or may not adjust their configuration parameters. Cells on line2914 are those that have equal total blame values, which is the sum ofthe up-blame values and the down-blame values assigned to each of thecells in this example.

FIG. 30 illustrates a flowchart of another embodiment method 3000 foradjusting cell configuration parameters in a wireless network havingmultiple cells based on blames. Generally, the method 3000 receives aplurality of MRs for the cells, identifies bad MRs for each of the cellsusing a set of pre-defined performance criteria of the wireless network,and assigns blames to responsible cells for each of the bad MRs based onanalysis of the corresponding bad MRs and other information, such ascell topology, antenna parameters, etc. In this example, two blamecategories, namely, down-blame and up-blame are used for assigningblames. The two blame categories are used only for illustrative purposeand should not be interpreted as limiting the scope of the claims. Themethod 3000 starts with step 3002, where the method 3000 receives MRs ofthe wireless network during a data collection period given a fixedconfiguration of the cells. A UE may transmits a MR to its serving cellperiodically (e.g., whenever pilots are received), when and asconfigured for pilot measurement reporting, or whenever requested. Inone embodiment, the method 3000 also collects other information, such asMDT data and topology information of each cell, which may be useful forassigning blames to the cells.

At step 3004, for each MR of a cell, the method 3000 determines whetherthe MR satisfies a cell coverage criteria, e.g., whether a RSRP, or anyother measure for reference signal strength, included in the MR isgreater than or equals a first threshold T1. If the RSRP is greater thanor equals the first threshold T1, then the method 3000 continues todetermine whether the MR satisfies a cell quality criteria at step 3006,e.g., whether a RSRQ, or any other measure for reference signal quality,included in the MR is greater than or equals a second threshold T2. Ifthe MR also satisfies the quality criteria, i.e., the RSRQ is greaterthan or equals the second threshold T2, the MR is marked as a good MR atstep 3008 and no blame will be assigned to any cell for this good MR.The method then goes back to step 3004 to determine whether a next MR ofthe cell is good or bad. The method may record the number of good MRsfor each cell, which may be used to estimate performance of the wirelessnetwork.

If the MR does not satisfy the cell coverage criteria at step 3004, orif the MR satisfies the cell coverage criteria at step 3004 but fails tosatisfy the cell quality criteria at the step 3006, the MR is marked badat step 3010. At step 3012, the method 3000 classifies the bad MR intoone of multiple MR categories, and assigns blames for the bad MR toresponsible cells. Bad MRs are classified into different categories sothat responsible cells may be identified and appropriate blame valuesmay be assigned. In one embodiment, a bad MR is classified into fourcategories: weak coverage with non-co-site neighbor, weak coveragewithout non-co-site neighbor, poor quality with non-co-site neighbor,and poor quality without non-co-site neighbor. The weak coverageindicates that the MR fails the cell coverage criteria, and the poorquality indicates that the MR fails the cell quality criteria.

A non-co-site neighbor of a cell is a neighbor of the cell which doesnot share the same base station with the cell. When a bad MR of a cellincludes RSRP information of its neighbors, i.e., the cell hasnon-co-site neighbors, interference or overshooting of its neighbors maybe considered when assigning blames for this bad MR. Each MR categorymay be further classified into different sub-categories, so that blamesmay be assigned appropriately to responsible cells. For example, theweak coverage without non-co-site neighbor is classified intosub-categories of weak interior and insufficient coverage. Domainexpertise, knowledge and experience may be used to define different MRcategories and sub-categories. A person of ordinary skill in the artwould recognize many variations, alternatives and modifications forcategorizing bad MRs for blame assignment.

Blames for a bad MR may be assigned to responsible cells based oninformation included in the MR, such as the RSRP list, RSRQ, timingadvance which may indicate distance of the UE reporting a bad MR to thecell (referred to as distance of the bad MR), topology information ofthe cell, information about the cell antenna, such as the main loberadius and planned radius, and other information. FIG. 31 illustrates adiagram of an embodiment antenna radiation coverage zone 3100 of a cell.As shown, an antenna 3110 of the cell has a main lobe or main beam 3120for communications.

In an example when a bad MR is in a category of weak coverage withnon-co-site neighbor, if the timing advance of the bad MR indicates thatthe distance of the bad MR falls in the downblame zone 3130, e.g., whenthe distance is less than a pre-defined down-blame distance threshold, adown-blame with a blame value “1” may be assigned to the cell. This isbecause the cell needs to decrease its antenna tilt in order to providesufficient coverage to the bad MR (i.e., the UE reporting the bad MR)which is closer to the antenna 3110 of the cell. If the timing advanceof the bad MR indicates that the distance of the bad MR falls in theupblame zone 3150, an up-blame with a blame value “1” may be assigned tothe cell, indicating an increased antenna tilt of the cell is desiredfor providing coverage to a MR far away from the antenna 3110. If thedistance of the bad MR falls in the non-action zone 3140, the blame isnot assigned. In this case, the blame for the bad MR is unknown, sinceit is not clear what causes the bad MR.

In some embodiments, for various reasons, a blame corresponding to a badMR of a cell is left un-assigned due to uncertainty or unknown rootcauses. In one embodiment, this un-assigned blame is accounted for inthe total blame of the cell (so that the total blame value of the cellis conserved, and the total blame value of the wireless network isconserved). In another embodiment, the un-assigned blames of a cell aredivided as additional up-blames and down-blames according to a ratio ofthe up-blames and down-blames to the total assigned blame value of thecell, and are allocated to the final sub-total up-blame value and thefinal sub-total down-blame value of the cell. For example, a cell has n1un-assigned blames (i.e., the un-assigned blame values are n1), asub-total up-blame value x1 and a sub-total down-blame value y1. Thetotal blame value of the cell is (n1+x1+y1), and the total assignedblame value of the cell is (x1+y1). The final sub-total up-blame valuemay be calculated by: x1+[x1/(x1+y1)]*n1, and the final sub-totaldown-blame value is equal to (total blame value of the cell−finalsub-total up-blame value), which is: (n1+x1+y1)−{x1+[x1/(x1+y1)]*n1}.This ensures that the up-action probability and the down-actionprobability of each cell remains the same regardless of whether theun-assigned blames are re-assigned or not.

Referring back to FIG. 30, after blames of a bad MR in the cell areassigned to responsible cells, the method 3000 will check whether allbad MRs of the cells in the wireless network are identified at step3014. If not, the method 3000 goes back to step 3004 to identify thenext bad MR of the cell, or a next bad MR of a next cell if all bad MRsof the cell are identified and have blames assigned. The steps of3004-3014 are repeated for each MR received in each of the cells in thewireless network. When all bad MRs received in the wireless networkduring the data collection period are identified and correspondingblames are assigned, the method 3000 proceeds to step 3016, where themethod 3000 calculates a sub-total up-blame value and a sub-totaldown-blame value for each of the cells. Calculation of the sub-totalup-blame value and the sub-total down-blame value may take into accountof the un-assigned blames of each corresponding cell. At step 3018, themethod 3000 calculates an up-action probability and a down-actionprobability for each of the cells, based on which an action may beassigned to the cells for adjusting configuration parameters of thecells.

Aspects of this disclosure may provide advantages over conventionalautomatic cell planner (ACP) solutions, which typically requireminimization of drive test (MDT) data with UE geo-location (AGPS) andaccurate antenna configuration parameters to achieve accuratepropagation modeling. Notably, systems for providing UE geo-locationinformation and accurate antenna configuration parameters may be costlyand require human verification. Accordingly, embodiment techniquesprovide cost savings by reconfiguring RF parameters without relying onUE geo-location and/or antenna configuration feedback information.

Aspects of this disclosure may maximize CCO objective functions underconstrained inputs. In some embodiments, techniques may utilizecontinuous closed loop measurement report (MR) feedback from a network.Drive tests (DT) and MDT data may also be used. Embodiment techniquesmay adjust RF configuration parameters without access to UE geo-location(AGPS), and without access to accurate antenna configuration parameters.Hence, embodiment techniques may offer similar accuracy to ACP CCO, butat a much lower cost.

Aspects of this disclosure provide a SON CCO algorithm. Embodimentalgorithms may calculate cell level features or blame metrics from MRs.Embodiment Algorithms may label coverage/quality/interference/overshootstatuses that provide mappings for “intuitively correct” adjustmentdecisions based on domain knowledge applied simultaneously on multiplecells. This may allow the algorithm to substantially increaseperformance in a relatively short time frame. Embodiment algorithms maycharacterize a cell's coverage status as good, weak, weak edge only,weak interior/insufficient only, weak edge and interior/insufficient.Embodiment algorithms may characterize a cell's interference status asmultiple interferer, single interferer, or non-interferer.

Aspects of this disclosure may provide a first phase of analyticsassisted SON algorithms for CCO to achieve machine learned cell labelsin addition to engineering knowledge guidelines for iterative actionsteps. This phase may be based at least partially on cell level featuresabstracted from MR data, labels or metrics of blame (e.g.,multi-interferer, single/medium interferer, over-shooter, etc.), whichmay be gleaned using unsupervised or semi-supervised learning methods.Aspects of this disclosure may provide a feedback loop for UE MRs thatsample the network state.

A clustering, machine learning algorithm that processes real-time localdata and historical global data that represents key cell features aspoints in a multi-dimensional space, and which groups similar pointstogether. Aspects of this disclosure may provide cell bottlenecklabeling or blame metric assignment. A Cell (a point) is given a labelbased on cluster membership, e.g., non-interferer vs. multipleinterferer, non-over-shooter vs. over-shooter. Alternatively, numericalblame metric and related blame action metric may be assigned.

Aspects of this disclosure may provide action rules that govern smallstep changes to cell parameters (power/tilt) in the “correct” direction.In white-box phase, engineering knowledge guides small step action basedon machine learned cell labels or blame action metrics. Actions aredesigned to increase the score with high probability initially.

Aspects of this disclosure may provide an AA SON Approach that usesautomatic software programming: to learn (online) the environment viareal-time feedback (of UE MRs and cell KPIs) and analytics; to abstractthe UE MR level information to cell level labels and metrics mapping todomain expertise guided incremental actions for optimizingconfiguration. Aspects of this disclosure provide a generalizableframework that is extendable to a variety of use cases, e.g., loadbalancing. Embodiment algorithms may provide significant improvement in10-20 iterations. Labeling and blame metrics show good correlation withactual interferers, over-shooters, coverage/quality challenged cellsetc.

Embodiment algorithms may calculates cell level features or blamemetrics from MRs, as well as label cells based on theircoverage/quality/interference/overshoot status.

Aspects of this disclosure provide an embodiment algorithm (version 2).The embodiment algorithm may be configured to record all positiveNormBAM(j) metrics gathered from multiple scenarios in a global databaseand cluster them (1-D) into different levels, e.g., three levels. Theembodiment algorithm may also be configured to map positive clusters toactions: The lowest magnitude clusters may be mapped to no action; themiddle clusters may be mapped to a single parameter action (e.g.,antenna down-tilt) by one step, and the highest clusters may be mappedto multiple or joint parameters (e.g., down-tilt and transmit powerreduction) by one step each. The embodiment algorithm may also clusternegatives in a global database. Specifically, the embodiment algorithmmay record all negative NormBAM(j) metrics in the global database andcluster them into multiple levels, e.g., three levels.

The embodiment algorithm may also map negative clusters to actions. Forexample, lowest magnitude clusters may be mapped to no action; middleclusters may be mapped to a single parameter (e.g., antenna up-tilt);and high clusters may be mapped to joint parameters (e.g., antenna up,power-up). The embodiment algorithm may also filter actions based on acurrent state. For example, the final action may be an adjustment of theaction based on the above cluster mapping, and may depend on the coarsecurrent estimated state (configuration). If a single parameter action issuggested (down-tilt or power-down), and current total tilt is estimatedto be already high then the action may adjust the power. If currentpower is also already low, then no actions may be taken.

The embodiment algorithm may divide problems UEs/MRs into the followingmutually exclusive categories:

Category 0 UEs/MRs have a weak coverage problem (best serving RSRP<−105dBm). Category 0 UEs/MRs may be further divided into category 0.1UEs/MRs that have weak edge coverage; and Category 0.2 UEs/MRs are thosethat are not in Category 0.1. Category 0.1 UEs may be defined as thesecond best RSRP>=best serving RSRP−6 dB. One unit of blame for UE u incategory 0.1 is assigned to its own best serving cell i (self blame).The sign is positive because weak edge coverage is mitigated by up-tiltand thus have weak interior/insufficient coverage. One unit of blame maybe assigned for each UE in category 0.2 based on its own best servingcell (e.g., self-blame). Typically weak interior versus insufficientcoverage results in opposite actions (down-tilt vs. up-tilt). If CODtriggers COC on a cell, then weak insufficient coverage can result witha positive sign for blame with action of up-tilt/up-power formitigation.

Category 1 UEs/MRs are those not in Category 0 that have the problem ofpoor quality (e.g., SINR<3 dB) due to a combination of serving cellweakness and other cell interference and is further divided intosub-categories: Initially, self blame S(RSRP(i)) is assigned to servingcell i depending on its strength using a sigmoidal function to computethe blame: S(x) is S(x)→1 as x→−105 dBm from above and S(x)=1 forx<=−105 dBm and similarly, S(x)→0 as x→−95 dBm from below and S(x)=0 forx>=−95 dBm; also at the mid-point: S(−100)=½. The remaining (other)blame 1−S(RSRP(i)) is divided among interfering cells, if any, based onthe following categories: Category 1.1 UEs/MRs are those not in category0 reporting the second best RSRP>=best serving RSRP−3 dB. For a UE ubest served by cell i, let C_(1.1)(u) be the set of all other cells suchthat their RSRP>=RSRP(i)−3 dB. Then the remaining (other) blame for u'spoor quality=(1−S(RSRP(i))/|C_(1.1)(u)| is equally divided between theseother cells. Thus if there is only one such cell, it is assigned theremaining blame regarding u. The rationale here is that even a singlecell at more than half the power of the best server will likely causethe SINR to drop below 3 dB.

Category 1.2 UEs/MRs are those not in categories 0 or 1.1 reporting thesecond best RSRP>=best serving RSRP−6 dB. For a UE u best served by celli, let C_(1.2)(u) be the set of all other cells such that theirRSRP>=RSRP(i)−6 dB. Then the remaining (other) blame for UE's poorquality=min(1/2,1/|C_(1.2)(u)|)*(1−S(RSRP(i)) is equally divided betweenthese other cells. Thus if there are two or more such other cells, theyare assigned to share the remaining (other) blame regarding u equally.The rationale here is that just two cells at more than quarter the powereach of the best server will cause the SINR to drop below 3 dB. Notethat if there is only one such other cell then its assignment is cappedat half the remaining blame—the unaccounted blame is left unassigned asan approximation.

Category 1.3 UEs/MRs are those not in categories 0 or 1.1 or 1.2reporting the second best RSRP>=best serving RSRP−9 dB. For a UE u bestserved by cell i, let C_(1.3)(u) be the set of all other cells such thattheir RSRP>=RSRP(i)−9 dB. Then the remaining (other) blame for UE's poorquality=min(1/4,1/|C_(1.3)(u)|))*(1−S(RSRP(i)) is equally dividedbetween these other cells. Thus if there are four or more such othercells, they are assigned to share the remaining (other) blame regardingu equally. The rationale here is that four cells at more than an eighthpower each of the best server will cause the SINR to drop below 3 dB.Note that if there is only one, two or three such other cells then theirassignment is capped at a quarter of the remaining blame—the unaccountedblame is left unassigned as an approximation. Category 2 UEs/MRs arethose not in Category 0 or 1 that have the problem of poor quality(SINR<3 dB).

Category 2 UEs/MRs are those not in categories 0 or 1. Self blame S(RSRP(i)) is assigned to a serving cell in a similar manner to category1 UEs. Such UEs do not have a clearly responsible interferer to assignremaining (other) blame and so the unaccounted blame is left unassignedas an approximation. Such instances are hopefully low—yet our algorithmwill monitor the numbers of such UEs and their accumulated unassignedblame at cell and system level.

Embodiment algorithms may assign a blame counter matrix to cells. FIG.32 illustrates an embodiment blame counter matrix. The rules forassigning blame metrics to cells are as follows:

-   Every best serving cell i has a set of served UEs/MRs S_(i);    S_(i)=S_(i,good) U S_(i,problem) (disjoint union);    S_(i,problem)=S_(i,0.1) U S_(i,0.2) U S_(i,1.1) U S_(i,1.2) U    S_(i,1.3) U S_(i,2)(disjoint union);-   Each of the UEs/MRs u belonging to a problem sub-category is    associated with a group of cells in the system that are assigned    blame;-   For category 0 UEs, the serving cell takes all the blame and for    category 1 UEs, some other cells also share part of the blame;-   For a fixed serving cell i and every cell j in the system, every    served UE/MR of i with a problem distributes the blame (one unit    maximum) for its problem across several or all js (including i);-   For a given pair of cells i and j, accumulate the individual blame    accorded to j over all UEs/MRs served by i and record in the B(i,j)    entry of the blame matrix; B(i,i) along the diagonal is the self    blame.-   Category 0 and 2.1 UEs contribute to only self blame whereas    Category 1 UEs contribute to other blame as well;-   For any given i (fixing a row), summation over j of B(i,j) is the    row-sum that is roughly equal to the number of problem UEs served by    i (could be less because some blame may be unassigned) related to    cell level O.F; and

The sum of all row-sums is roughly equal to the total number of problemUEs in the system’ For any given j (fixing a column), summation over iof B(i,j) is the column-sum that is roughly equal to the number ofproblem UEs caused by j. This is the Blame Metric of cell j, BM(j).

Instead of using the blame metric directly for action, some embodimentalgorithms may use the blame action metric to exploit the fact that forself-blame, the action is typically opposite (up-tilt/power-up) to thatof the action to mitigate other-blame (down-tilt/power-down). Reflectingthis opposite action, the Blame Action Metric of cell j is (for example)defined as: BAM(j)=Σ_(i< >j) B(i,j)−B(j,j) (same cell blame is negativeweighted); BAM(j)=Σ_(< >j, eNB(i)< >eNB(j)) B(i,j)−B(j,j) (other cellsof the same eNB are zero weighted); Normalize BAM(j) by the total numberof UEs in the cluster formed by j and all its neighbors (can useneighbor list or infer it based on significant BM(i,j) values:NormBAM(j)=BAM(j)/Number of UEs in j and all of its RF neighbors. IfNormBAM(j) is small in magnitude, there may be no action on cell j. Itis possible to normalize BM(j) to yield NormBM(j). NormBM(j) providessome information about the cell. For instance, if a cell j has highNormBM(j) but low NormBAM(j) then that cell is in a very “tricky” or“ambiguous” action situation where its numerous problem UEs arerequiring conflicting actions that essentially cancel out. If a cell hasboth of them high, then that cell is a problem cell but with a clearaction for resolution. If a cell has both of them low, then that cell isnot a problem cell.

Aspects of this disclosure may use an action rule for mapping a NormBAMmetric to actions. Clustering of Positives in Global Database: Allpositive NormBAM(j) metrics gathered from multiple scenarios may berecorded in a global database and clustered into multiple levels, e.g.,three levels. Mapping Positive Clusters to Actions: The lowest magnitudeclusters map to no action; the middle cluster maps to single parameteraction (e.g., antenna down-tilt) by one step and the highest clustermaps to joint parameter (e.g., down-tilt and power-down) action by onestep each. Clustering of Negatives in Global Database: NegativeNormBAM(j) metrics may be clustered in the global database and clusterthem into multiple levels, e.g., three levels. Mapping Negative Clustersto Actions: Lowest magnitude cluster implies no action; middle one tosingle parameter action (Antenna up-tilt preferred) by one step andhighest cluster maps to joint parameter (up) action by a step each.Filtering Actions Based on Current State: The final action is anadjustment of the action based on the above cluster mapping that dependson the coarse current estimated state (configuration) For example, ifsingle parameter action is suggested (down-tilt or power-down), andcurrent total tilt is estimated to be already high then power-down isdone. If current power is also already low, then no action is taken.

Aspects of this disclosure may use semi-supervised learning (EM) toaugment clustering for improved thresholding of the NormBAM metric,e.g., NormBAMs of good performing cells (low interferer, high quality,good coverage) taken from optimized configurations can provide labeledtraining examples in the “no action” range of points. The “dead zone”range of “no action” configuration states can be narrowed to be next tonothing (to essentially allow tilt and power actions to be unconstrainedexcept for max and min allowed e-tilts and powers). In other words, ife-tilt range allowed by the vendor is [0, 12] degrees, then we mayprescribe the small range of tilt as (−inf, 0], the moderate range as(0, 12) and the high range as [12, inf).

Embodiment algorithms may provide ways to deal with overshooting cells.They may be discovered by a similar learning procedure of the algorithm(version 1). The first way is to incorporate overshooting into the blamemetric. More specifically, after assigning blame in category 0.x, weconsider a new category 0.3 in which falls those UEs/MRs that are servedby cell i but should not be (since i is an overshooter or overloaded).Thus when the serving cell (of a UE/MR at “large” distance) is itselfthe overshooter, we decrement B(i,i) by 1 keeping in mind that B(i,i)positively influences up-tilt/power-up (or increment B(j,i) by 1 where jis the strongest local cell). If an overshoot UE/MR (overlap at “large”distance) falls in category 1.x, we can continue the blame sharing asbefore or punitively assign all “remaining” or full blame to theovershooting cell (i.e., increment B(i,j) by 1−S(RSRP(i)) or 1 for eachUE/MR served by cell i that is overlapped by overshooting cell j). Wealso optionally add a category 3.1 of blame assignment for overshootUEs/MRs not falling under categories 0 or 1 (by adding/subtracting a newunit of blame to other/self over-shooters), i.e., even if that UE/MRreports no coverage/quality issues. The second way is to provide aseparate Label for overshooter: If cell j is deemed overshooter andAction prescribed for cell j based on BAM(j) is down-tilt/power-down,then do nothing further. If Action prescribed for cell j based on BAM(j)is do nothing, then modify to single parameter down-tilt/power-down. IfAction prescribed for cell j based on BAM(j) is up-tilt/power-up thencancel it to no action.

Embodiment algorithms may provide actions for configuring cells havingexcessive up-tilt and power-up parameters. The up-tilt/power-up actionarises from a need to improve a cell's own coverage or quality. Aconsequence of this is the interference increase in UE/MRs served byneighboring cells and/or overshoot problems. Such action is selected tocorrect any imbalance between selfishness (for improving current servedUEs' problems) and cooperation (for improving current interfered UEs ofother cells). However, such action does not account for the consequentincrease in number of interfered UEs in other cells. Several cells inthe same area (neighbors of each other or have common neighbors) beingup-tilted/powered-up at the same time may lend a multiplier effect tosuch increase in interfered UEs and worsen quality. This can createinstability in system performance when successive similar actions runaway (due to competition between neighbors) or successive oppositeactions on a cell engender oscillations with no meaningful improvement.

Embodiment algorithms may address this issue by implementing thefollowing steps. Construct an interaction graph GU of up-tilt/power-upcandidate cell nodes with edges between them if they are neighbors orhave significantly interacting common neighbors. Use the B(i,j) matrix(e.g., blame metric) as a guideline for figuring out “significant”interacting neighbors. In other words, the adjacency matrix AU(i,j) forgraph GU is a function of B(i,j), B(i, neighbor of j) and B(neighbor ofi, j). Note that GU is not the original network graph NG. GU has asubset of nodes of NG but with a superset of edges given a node. Thecomplementary graph GU′ replaces edges with non-edges and non-edges withedges. The problem is then to find the Maximum Clique of GU′ (largestcomplete sub-graph or largest set of mutually inter-connected nodes).Maximum Clique is known to be an NP Hard Problem. Use a suitableheuristic for maximal clique (with high degree vertices for asub-optimal solution: R implementations for max clique; GU″=approximateMax Clique of GU′ is a limited set of cells that can be used forUp-tilt/power-up Actions with reduced worry of unstable interaction thatcould increase interference

Embodiment algorithms may mitigate instability by picking separatedcells for Down-tilt/Power-Down. Specifically, a problem may exist forthose cells in the system identified for down-tilt/power-down, asidentifying the largest subset of them such that they are not directneighbors may cause down-tilting to open up edge holes.

Embodiment algorithms may address this issue by implementing thefollowing steps. Construct an interaction graph GD ofdown-tilt/power-down candidate cell nodes with edges between them ifthey are neighbors. Use the B(i,j) matrix, i.e., blame metric as aguideline for figuring out neighbors. In other words, the adjacencymatrix AD(i,j) for graph GD is a function of B(i,j). Note that GD is notthe original network graph NG. GD has a subset of nodes of NG with sameedges given a node. The complementary graph GD′ replaces edges withnon-edges and non-edges with edges The problem is then to find theMaximum Clique of GD′ (largest complete sub-graph or largest set ofmutually inter-connected nodes). Maximum Clique is known to be an NPHard Problem. Use a good heuristic for maximal clique (with high degreevertices) for sub-optimal solution: R implementations for max clique:GD″=approximate Max Clique of GD′ is a limited set of cells that can beused for down-tilt/power-down Actions with reduced worry of unstableinteraction that could cause coverage/quality holes. FIG. 33 illustratesa diagram of a Graph Problem & Solution Visualization.

Embodiment algorithms may learn from mistakes through maximizing gain inNormBM(j) and resultant M-out-of-N cell tuning. Usually NormBM(j) is agood indicator of cells that are root causes of problem UEs. ActionCells may have High NormBM(j). Actions on Cells are chosen with theexpectation that their NormBM(j) is reduced (step-by-step). NormBM(j)reduction is tied to System Objective Function Improvement. However,actions taken on chosen cells are not guaranteed to reduce theirNormBM(j) due to unknown hidden variables. In practice, NormBM(j) maynot drop consistently or may even grow (due to interactions and hiddenvariable impacts). The algorithm may learn which cells j under whichconfigurations under which current NormBM(j) and NormBAM(j) (action)values produce the largest reduction (Gain) in NormBM(j) on average.Initially target precisely such cells for WB action (M-out-of-N forWhitebox). Cells that produce extreme/sustained negative gain may beremoved from Whitebox list first for no action and then passed on toBlackbox for Oppositional, Exploitative and Explorative Action. FIG. 34illustrates a flowchart of a method for operating a CCO interface.Additional details regarding aspects of this disclosure are provided inthe Appendix filed herewith.

FIG. 35 illustrates a block diagram of an embodiment controller 3500adapted to iteratively generate and evaluate global solutions. As shown,the embodiment controller 3500 includes one or more ingress interfaces3501, one or more egress interfaces 3502, a global solution generator3510, an SA-based global solution evaluator 3560, a local solutionevaluator 3570, and a global solution selector 3595. The one or moreingress interfaces 3501 may be configured to receive information (e.g.,measurement reports, etc.) from devices (e.g., APs) in a wirelessnetwork, and the one or more egress interfaces 3502 may be configured tosend local solutions to devices (e.g., APs) in the wireless network. Theglobal solution generator 3510 may include hardware and/or softwareadapted to generate global solutions based at least in part oninformation received over the one or more ingress interfaces 3501.

In this example, the global solution generator 3510 includes a localsolution generator 3520. The local solution generator 3520 may includehardware and/or software adapted to generate local solutions based atleast in part on information received over the one or more ingressinterfaces 3501. The local solution generator 3520 may be capable ofgenerating local solutions in a variety of different ways. For example,the local solution generator 3520 may use an SA-based generator 3520 togenerate local solutions in accordance with an SA-based algorithm. Insuch an example, the local solution generator 3520 may use a localparameter adjustment unit 3534 to set and/or adjust a step size and/orstep direction for one or more wireless configuration parameters in alocal solution. The local solution generator 3520 may then use aneighbor locator to generate a new local solution based on a currentlocal solution and the step size/directions set by the local parameteradjustment unit 3534. As another example, the local solution generator3520 may use a random generator 3540 to randomly select wirelessconfiguration parameters of a new local solution. As another example,the local solution generator 3520 may generate a new local solutionbased on Gaussian algorithm 3550.

The SA-based global solution evaluator 3560 may include hardware and/orsoftware adapted to evaluate global solutions generated by the globalsolution generator 3510 in accordance with a SA-based algorithm. In thisexample, the SA-based global solution evaluator 3560 includes a globalacceptance probability adjuster 3562 adapted to set and/or adjust aglobal acceptance probability of the SA-based algorithm and a globalcost calculator 3564 adapted to calculate a global cost based on anobjective function.

The local solution evaluator 3570 may include hardware and/or softwareadapted to evaluate local solutions. In some embodiments, the localsolution evaluator 3570 may evaluate local solutions based on anSA-based algorithm 3580. In such embodiments, the local solution isevaluated based on a function of a cost computed by the local costcalculator 3584 and an acceptance probability set by the localacceptance adjuster 3582. In other embodiments, the local solutionevaluator 3570 may evaluate local solutions based on a cost thresholdusing the threshold based evaluator 3590.

The global solution selector 3595 may include hardware and/or softwareadapted to select one of the global solutions that was evaluated duringa sequence of iterations. As mentioned above, components of theembodiment controller 3500 may be hardware, software, or a combinationthereof. Each component may be referred to as a unit or module. In oneembodiment, one or more components of the embodiment controller 3500 areintegrated circuits, such as field programmable gate arrays (FPGAs) orapplication-specific integrated circuits (ASICs).

FIG. 36 illustrates a block diagram of an embodiment processing system3600 for performing methods described herein, which may be installed ina host device. As shown, the processing system 3600 includes a processor3604, a memory 3606, and interfaces 3610-3614, which may (or may not) bearranged as shown in FIG. 36. The processor 3604 may be any component orcollection of components adapted to perform computations and/or otherprocessing related tasks, and the memory 3606 may be any component orcollection of components adapted to store programming and/orinstructions for execution by the processor 3604. In an embodiment, thememory 3606 includes a non-transitory computer readable medium. Theinterfaces 3610, 3612, 3614 may be any component or collection ofcomponents that allow the processing system 3600 to communicate withother devices/components and/or a user. For example, one or more of theinterfaces 3610, 3612, 3614 may be adapted to communicate data, control,or management messages from the processor 3604 to applications installedon the host device and/or a remote device. As another example, one ormore of the interfaces 3610, 3612, 3614 may be adapted to allow a useror user device (e.g., personal computer (PC), etc.) tointeract/communicate with the processing system 3600. The processingsystem 3600 may include additional components not depicted in FIG. 36,such as long term storage (e.g., non-volatile memory, etc.).

In some embodiments, the processing system 3600 is included in a networkdevice that is accessing, or part otherwise of, a telecommunicationsnetwork. In one example, the processing system 3600 is in a network-sidedevice in a wireless or wireline telecommunications network, such as abase station, a relay station, a scheduler, a controller, a gateway, arouter, an applications server, or any other device in thetelecommunications network. In other embodiments, the processing system3600 is in a user-side device accessing a wireless or wirelinetelecommunications network, such as a mobile station, a user equipment(UE), a personal computer (PC), a tablet, a wearable communicationsdevice (e.g., a smartwatch, etc.), or any other device adapted to accessa telecommunications network.

In some embodiments, one or more of the interfaces 3610, 3612, 3614connects the processing system 3600 to a transceiver adapted to transmitand receive signaling over the telecommunications network. FIG. 37illustrates a block diagram of a transceiver 3700 adapted to transmitand receive signaling over a telecommunications network. The transceiver3700 may be installed in a host device. As shown, the transceiver 3700comprises a network-side interface 3702, a coupler 3704, a transmitter3706, a receiver 3708, a signal processor 3710, and a device-sideinterface 3712. The network-side interface 3702 may include anycomponent or collection of components adapted to transmit or receivesignaling over a wireless or wireline telecommunications network. Thecoupler 3704 may include any component or collection of componentsadapted to facilitate bi-directional communication over the network-sideinterface 3702. The transmitter 3706 may include any component orcollection of components (e.g., up-converter, power amplifier, etc.)adapted to convert a baseband signal into a modulated carrier signalsuitable for transmission over the network-side interface 3702. Thereceiver 3708 may include any component or collection of components(e.g., down-converter, low noise amplifier, etc.) adapted to convert acarrier signal received over the network-side interface 3702 into abaseband signal. The signal processor 3710 may include any component orcollection of components adapted to convert a baseband signal into adata signal suitable for communication over the device-side interface(s)3712, or vice-versa. The device-side interface(s) 3712 may include anycomponent or collection of components adapted to communicatedata-signals between the signal processor 3710 and components within thehost device (e.g., the processing system 3600, local area network (LAN)ports, etc.).

The transceiver 3700 may transmit and receive signaling over any type ofcommunications medium. In some embodiments, the transceiver 3700transmits and receives signaling over a wireless medium. For example,the transceiver 3700 may be a wireless transceiver adapted tocommunicate in accordance with a wireless telecommunications protocol,such as a cellular protocol (e.g., long-term evolution (LTE), etc.), awireless local area network (WLAN) protocol (e.g., Wi-Fi, etc.), or anyother type of wireless protocol (e.g., Bluetooth, near fieldcommunication (NFC), etc.). In such embodiments, the network-sideinterface 3702 comprises one or more antenna/radiating elements. Forexample, the network-side interface 3702 may include a single antenna,multiple separate antennas, or a multi-antenna array configured formulti-layer communication, e.g., single input multiple output (SIMO),multiple input single output (MISO), multiple input multiple output(MIMO), etc. In other embodiments, the transceiver 3700 transmits andreceives signaling over a wireline medium, e.g., twisted-pair cable,coaxial cable, optical fiber, etc. Specific processing systems and/ortransceivers may utilize all of the components shown, or only a subsetof the components, and levels of integration may vary from device todevice.

It may be advantageous to set forth definitions of certain words andphrases used throughout this patent document. The terms “include” and“comprise,” as well as derivatives thereof, mean inclusion withoutlimitation. The term “or” is inclusive, meaning and/or. The phrases“associated with” and “associated therewith,” as well as derivativesthereof, mean to include, be included within, interconnect with,contain, be contained within, connect to or with, couple to or with, becommunicable with, cooperate with, interleave, juxtapose, be proximateto, be bound to or with, have, have a property of, or the like.

Although the description has been described in detail, it should beunderstood that various changes, substitutions and alterations can bemade without departing from the spirit and scope of this disclosure asdefined by the appended claims. Moreover, the scope of the disclosure isnot intended to be limited to the particular embodiments describedherein, as one of ordinary skill in the art will readily appreciate fromthis disclosure that processes, machines, manufacture, compositions ofmatter, means, methods, or steps, presently existing or later to bedeveloped, may perform substantially the same function or achievesubstantially the same result as the corresponding embodiments describedherein. Accordingly, the appended claims are intended to include withintheir scope such processes, machines, manufacture, compositions ofmatter, means, methods, or steps.

The following references are related to subject matter of the presentapplication. Each of these references is incorporated herein byreference in its entirety:

2008 International Symposium on Telecommunications publication entitled“A Modified Very Fast Simulated Annealing Algorithm”

Signal Processing Journal Publication entitled “Adaptive simulatedannealing for optimization in signal processing applications”

Areso pamphlet entitled “The Critical Importance of Subscriber-centricLocation Data for SON Use Cases”

Reverb Networks publication entitled “Antenna Based Self OptimizingNetworks for Coverage and Capacity Optimization

IEEE publication entitled “UMTS Optimum Cell Load Balancing forInhomogeneous Traffic Patterns”

While this invention has been described with reference to illustrativeembodiments, this description is not intended to be construed in alimiting sense. Various modifications and combinations of theillustrative embodiments, as well as other embodiments of the invention,will be apparent to persons skilled in the art upon reference to thedescription. It is therefore intended that the appended claims encompassany such modifications or embodiments.

What is claimed is:
 1. A method for adjusting communication parametersin a multi-cell wireless network, the method comprising: iterativelygenerating and evaluating global solutions for a wireless network over asequence of iterations, each of the global solutions including multiplelocal solutions that specify wireless configuration parameters for localcoverage areas in the wireless network, wherein each of the globalsolutions are evaluated using a simulated annealing based (SA-based)optimization algorithm, wherein at least some of the local solutions areevaluated separately from the corresponding global solution according tolocal performance criteria, and wherein iteratively generating andevaluating the global solutions comprises: evaluating each of the globalsolutions during a corresponding iteration using the SA-basedoptimization algorithm; and evaluating local solutions in correspondingglobal solutions that satisfy a global performance criteria; selectingone of the global solutions when an output condition is reached; andsending local solutions in the selected global solution to access points(APs) in the wireless network.
 2. The method of claim 1, whereinevaluating the local solutions in the corresponding global solutionsthat satisfy the global performance criteria comprises: determiningwhether the local solutions satisfy a local performance criteria; andreplacing, in the corresponding global solutions, local solutions thatfail to satisfy the local performance criteria with previous localsolutions.
 3. The method of claim 2, wherein determining whether thelocal solutions satisfy a local performance criteria comprisesdetermining whether a cost of the local solution exceeds a threshold. 4.The method of claim 2, wherein determining whether the local solutionssatisfy a local performance criteria comprises determining whether afunction of a cost of the local solution and a local temperatureparameter of the SA-based optimization algorithm exceeds a threshold. 5.The method of claim 1, wherein iteratively generating and evaluating theglobal solutions further comprises: updating a global temperatureparameter of the SA-based optimization algorithm based on an evaluationof a global solution during an instant iteration; and updating one ormore local temperature parameters of the SA-based optimization algorithmbased on evaluations of one or more local solutions during the instantiteration.
 6. The method of claim 5, wherein the updated globaltemperature parameter defines a global acceptance probability used toevaluate global solutions during subsequent iterations, and wherein theone or more updated local temperature parameters define local step sizesand directions for modifying wireless parameters of current localsolutions when generating new local solutions during subsequentiterations.
 7. The method of claim 5, wherein the updated globaltemperature parameter defines a global acceptance probability used toevaluate global solutions during subsequent iterations, and wherein theone or more updated local temperature parameters define local acceptanceprobability used to evaluate local solutions during subsequentiterations.
 8. The method of claim 1, wherein iteratively generating andevaluating the global solutions over the sequence of iterations furthercomprises: generating a first new global solution during a firstiteration by finding neighbors of current local solutions in a currentglobal solution; identifying one or more new local solutions in thefirst new global solution that fail to satisfy local performancecriteria; replacing, in the first new global solution, the one or morenew local solutions with corresponding ones of the current localsolutions from the current global solution, thereby obtaining a modifiedglobal solution that includes at least one new local solution and atleast one current local solution; and generating a second new globalsolution during a second iteration by finding neighbors of localsolutions in the modified global solution.
 9. The method of claim 1,wherein the SA-based optimization algorithm is a coverage and capacityoptimization (CCO) algorithm, and wherein the global solutions specifyantenna tilts and transmit power levels for local coverage areas in thewireless network.
 10. The method of claim 1, wherein the SA-basedoptimization algorithm is an inter-cell interference coordination (ICIC)optimization algorithm, and wherein the global solutions specify one ormore of a sub-band power factor, edge-to-center boundary, and transmitpower levels for local coverage areas in the wireless network.
 11. Themethod of claim 1, wherein the optimization algorithm is a mobilityoptimization algorithm, and wherein the global solutions specifyhandover parameters for the local coverage areas.
 12. The method ofclaim 1, wherein the local solutions and the global solution areevaluated in the wireless network during test periods.
 13. A controllercomprising: a processor; and a non-transitory computer readable storagemedium storing programming for execution by the processor, theprogramming including instructions to: iteratively generate andevaluating global solutions for a wireless network over a sequence ofiterations, each of the global solutions including multiple localsolutions that specify wireless configuration parameters for localcoverage areas in the wireless network, wherein each of the globalsolutions are evaluated using a simulated annealing based (SA-based)optimization algorithm, wherein at least some of the local solutions areevaluated separately from the corresponding global solution according tolocal performance criteria, and wherein iteratively generating andevaluating the global solutions comprises: evaluating each of the globalsolutions during a corresponding iteration using the SA-basedoptimization algorithm; and evaluating local solutions in correspondingglobal solutions that satisfy a global performance criteria; select oneof the global solutions when an output condition is reached; and sendlocal solutions in the selected global solution to access points (APs)in the wireless network.
 14. The controller of claim 13, wherein theinstructions to evaluate the local solutions in corresponding globalsolutions that satisfy the global performance criteria includesinstructions to: determine whether the local solutions satisfy a localperformance criteria; and replace, in the corresponding globalsolutions, local solutions that fail to satisfy the local performancecriteria with previous local solutions.
 15. The controller of claim 13,wherein the instructions to iteratively generate and evaluate the globalsolutions further include instructions to: update a global temperatureparameter of the SA-based optimization algorithm based on an evaluationof a global solution during an instant iteration, wherein the updatedglobal temperature parameter defines a global acceptance probabilityused to evaluate global solutions during subsequent iterations; andupdate one or more local temperature parameters of the SA-basedoptimization algorithm based on evaluations of one or more localsolutions during the instant iteration, wherein the one or more updatedlocal temperature parameters define local step sizes and directions formodifying wireless parameters of current local solutions when generatingnew local solutions during subsequent iterations.
 16. The controller ofclaim 13, wherein the updated global temperature parameter defines aglobal acceptance probability used to evaluate global solutions duringsubsequent iterations, and wherein the one or more updated localtemperature parameters define local acceptance probability used toevaluate local solutions during subsequent iterations.
 17. A method foriteratively modifying wireless configuration parameters in a wirelessnetwork over a sequence of intervals, the method comprising: selectingsubsets of local coverage areas in the wireless network, each of thesubsets of local coverage areas corresponding to a different subset ofintervals in the sequence of intervals; iteratively generating andevaluating, during each subset of intervals, local solutions for thecorresponding subset of local coverage areas using a simulated annealingbased (SA-based) optimization algorithm without modifying wirelessconfiguration parameters of local coverage areas that are excluded fromthe corresponding subset of local coverage areas during thecorresponding subset of intervals; selecting a set of local solutionswhen an output condition is reached, wherein selecting the subsets oflocal coverage areas in the wireless network comprises calculating costsassociated with wireless transmissions in the local coverage areas priorto each subset of intervals, and selecting local coverage areas having acost exceeding a threshold for inclusion in the corresponding subset oflocal coverage areas; and sending the selected set of local solutionsspecified by a selected global solution to access points (APs) in thewireless network.
 18. The method of claim 17, wherein each subset oflocal coverage areas includes fewer than all local coverage areas in thewireless network.
 19. An apparatus comprising: a processor; and anon-transitory computer readable storage medium storing programming forexecution by the processor, the programming including instructions to:select subsets of local coverage areas in a wireless network, each ofthe subsets of local coverage areas corresponding to a different subsetof intervals in a sequence of intervals; iteratively generate andevaluate, during each subset of intervals, local solutions for thecorresponding subset of local coverage areas using a simulated annealingbased (SA-based) optimization algorithm without modifying wirelessconfiguration parameters of local coverage areas that are excluded fromthe corresponding subset of local coverage areas during thecorresponding subset of intervals; select a set of local solutions whenan output condition is reached, wherein the instructions to select thesubsets of local coverage areas in the wireless network includeinstructions to calculate costs associated with wireless transmissionsin the local coverage areas prior to each subset of intervals, andselect local coverage areas having a cost exceeding a threshold forinclusion in the corresponding subset of local coverage areas; and sendthe selected set of local solutions specified by a selected globalsolution to access points (APs) in the wireless network.
 20. Theapparatus of claim 19, wherein each subset of local coverage areasincludes fewer than all local coverage areas in the wireless network.